Correlation.ipynb

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      "cell_type": "markdown",
      "source": [
        "#In this EDA, I gonna explore the Correlation between temperature and cold, hot drinks sale.\n"
      ],
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              "   Day_No  Temp  Cold_Drink  Hot_Drink  Snacks\n",
              "0       1    35         120        175      45\n",
              "1       2    35         122        170      50\n",
              "2       3    36         125        172      51\n",
              "3       4    36         130        170      52\n",
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              "  "
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      ],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/correlation.csv')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are few methods to calculate correlation in statistics \n",
        "\n",
        "*   Pearson\n",
        "*   Kendall\n",
        "*   Spearman \n",
        "\n",
        "The result of the calculated values may differ a bit due to different mehods\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "a-MAWG0YYROJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's explore each of them"
      ],
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        "id": "PZreUPUwZnLp"
      }
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      "source": [
        "data.corr(method='pearson', min_periods=1)"
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      "execution_count": 2,
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              "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
              "Day_No      1.000000  0.269834    0.378274  -0.468887  0.045251\n",
              "Temp        0.269834  1.000000    0.890255  -0.734043 -0.021266\n",
              "Cold_Drink  0.378274  0.890255    1.000000  -0.756659  0.042191\n",
              "Hot_Drink  -0.468887 -0.734043   -0.756659   1.000000 -0.100323\n",
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              "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
              "Day_No      1.000000  0.189172    0.275782  -0.341609  0.009087\n",
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      "cell_type": "code",
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        "data.corr(method='spearman', min_periods=1)"
      ],
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        "id": "E9YelZqzZxX6",
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      },
      "execution_count": 6,
      "outputs": [
        {
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              "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
              "Day_No      1.000000  0.264297    0.380530  -0.457385  0.009190\n",
              "Temp        0.264297  1.000000    0.870657  -0.690304 -0.043479\n",
              "Cold_Drink  0.380530  0.870657    1.000000  -0.686986  0.029932\n",
              "Hot_Drink  -0.457385 -0.690304   -0.686986   1.000000 -0.045372\n",
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              "\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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Intuitively, we all know temperature and code, hot drinks sale is correlate. So let's figure it out by calculated numbers aka how they correlate."
      ],
      "metadata": {
        "id": "sX4qL2C_aGY5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Temp', 'Cold_Drink']].corr()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "GKN-rMVpas1z",
        "outputId": "cc99b1f4-8d2d-4238-8340-1d179c72947b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Temp  Cold_Drink\n",
              "Temp        1.000000    0.890255\n",
              "Cold_Drink  0.890255    1.000000"
            ],
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              "      <th>Temp</th>\n",
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              "      <th>Temp</th>\n",
              "      <td>1.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cold_Drink</th>\n",
              "      <td>0.890255</td>\n",
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              "            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",
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            ]
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Temp', 'Hot_Drink']].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-vEmYf0ZbM1T",
        "outputId": "82b46cb3-2c29-476b-80c3-2fb7fe484399"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Temp  Hot_Drink\n",
              "Temp       1.000000  -0.734043\n",
              "Hot_Drink -0.734043   1.000000"
            ],
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, How about the temperature and snack? Intuitively, it makes sense that temperature and snack sales aren't correlated.\n",
        "Le's check the calculated statistics if our intuition is correct."
      ],
      "metadata": {
        "id": "rza4DTF7bRE6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Temp', 'Snacks']].corr()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-7zWywuycAWc",
        "outputId": "008a569e-8f80-4538-f6ad-83e54d67b5b5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Temp    Snacks\n",
              "Temp    1.000000 -0.021266\n",
              "Snacks -0.021266  1.000000"
            ],
            "text/html": [
              "\n",
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              "      <th>Temp</th>\n",
              "      <th>Snacks</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>Temp</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Snacks</th>\n",
              "      <td>-0.021266</td>\n",
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              "\n",
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              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56fcd1c0-5b14-479c-aa03-b2bf6dafa161');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numbers are far from one and minus one, we can conclude that these two variables has no correlation. What wonderful our intuition is."
      ],
      "metadata": {
        "id": "Oky0hf5RcLSw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the visualization of our coorelation."
      ],
      "metadata": {
        "id": "YdD9FQWRc-WC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr().style.background_gradient(cmap='coolwarm').set_precision(3)"
      ],
      "metadata": {
        "colab": {
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          "height": 241
        },
        "id": "bjscHLE9cKhE",
        "outputId": "5426da14-99e6-43bc-a604-d2dfdda144dc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "#T_5d724_row2_col1 {\n",
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              "  color: #f1f1f1;\n",
              "}\n",
              "#T_5d724_row2_col3, #T_5d724_row3_col0, #T_5d724_row3_col1, #T_5d724_row3_col2, #T_5d724_row3_col4 {\n",
              "  background-color: #3b4cc0;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Day_No</th>\n",
              "      <th class=\"col_heading level0 col1\" >Temp</th>\n",
              "      <th class=\"col_heading level0 col2\" >Cold_Drink</th>\n",
              "      <th class=\"col_heading level0 col3\" >Hot_Drink</th>\n",
              "      <th class=\"col_heading level0 col4\" >Snacks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_5d724_level0_row0\" class=\"row_heading level0 row0\" >Day_No</th>\n",
              "      <td id=\"T_5d724_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
              "      <td id=\"T_5d724_row0_col1\" class=\"data row0 col1\" >0.270</td>\n",
              "      <td id=\"T_5d724_row0_col2\" class=\"data row0 col2\" >0.378</td>\n",
              "      <td id=\"T_5d724_row0_col3\" class=\"data row0 col3\" >-0.469</td>\n",
              "      <td id=\"T_5d724_row0_col4\" class=\"data row0 col4\" >0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5d724_level0_row1\" class=\"row_heading level0 row1\" >Temp</th>\n",
              "      <td id=\"T_5d724_row1_col0\" class=\"data row1 col0\" >0.270</td>\n",
              "      <td id=\"T_5d724_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
              "      <td id=\"T_5d724_row1_col2\" class=\"data row1 col2\" >0.890</td>\n",
              "      <td id=\"T_5d724_row1_col3\" class=\"data row1 col3\" >-0.734</td>\n",
              "      <td id=\"T_5d724_row1_col4\" class=\"data row1 col4\" >-0.021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5d724_level0_row2\" class=\"row_heading level0 row2\" >Cold_Drink</th>\n",
              "      <td id=\"T_5d724_row2_col0\" class=\"data row2 col0\" >0.378</td>\n",
              "      <td id=\"T_5d724_row2_col1\" class=\"data row2 col1\" >0.890</td>\n",
              "      <td id=\"T_5d724_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
              "      <td id=\"T_5d724_row2_col3\" class=\"data row2 col3\" >-0.757</td>\n",
              "      <td id=\"T_5d724_row2_col4\" class=\"data row2 col4\" >0.042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5d724_level0_row3\" class=\"row_heading level0 row3\" >Hot_Drink</th>\n",
              "      <td id=\"T_5d724_row3_col0\" class=\"data row3 col0\" >-0.469</td>\n",
              "      <td id=\"T_5d724_row3_col1\" class=\"data row3 col1\" >-0.734</td>\n",
              "      <td id=\"T_5d724_row3_col2\" class=\"data row3 col2\" >-0.757</td>\n",
              "      <td id=\"T_5d724_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
              "      <td id=\"T_5d724_row3_col4\" class=\"data row3 col4\" >-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5d724_level0_row4\" class=\"row_heading level0 row4\" >Snacks</th>\n",
              "      <td id=\"T_5d724_row4_col0\" class=\"data row4 col0\" >0.045</td>\n",
              "      <td id=\"T_5d724_row4_col1\" class=\"data row4 col1\" >-0.021</td>\n",
              "      <td id=\"T_5d724_row4_col2\" class=\"data row4 col2\" >0.042</td>\n",
              "      <td id=\"T_5d724_row4_col3\" class=\"data row4 col3\" >-0.100</td>\n",
              "      <td id=\"T_5d724_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "qk0WbXNJdbq5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(data['Temp'], data['Cold_Drink'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pz_Esx4VdHKN",
        "outputId": "933799c1-30ad-4348-c262-a041b0fd618c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(data['Temp'], data['Hot_Drink'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "rzLm9fAOdH6L",
        "outputId": "ac30dc96-096e-4e4c-ca80-7052d35514be"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAb4ElEQVR4nO3df5Dc9X3f8edLhxAnN+iq6ojRSRdRWagFiyD5LCAaN4KWChM3uoLTSIODYzuodnAztjs4qGii2oWBiRK7dj3GA0WVKaqAFHJlMDbFpakSDYI5WRgBRrYov3RQJI8s/AMhQLz7x34P7Z127273vrffz+6+HjM3s/vevdu3vrv71vf7+X4+768iAjMzay3Tik7AzMzy5+JuZtaCXNzNzFqQi7uZWQtycTcza0Eu7mZmLWjc4i5ps6QDkp4si50raaekxyUNSlqexSXp65L2SXpC0rKpTN7MzCqbyJ77FuCSUbE/B74UEecCf5bdB/gwsCj7WQfcnE+aZmZWi5PGe0JEbJe0YHQYODW7PQt4Obu9Grg9SiujdkrqknR6RLwy1mvMmTMnFiwY/RJmZjaWXbt2/TQiuis9Nm5xr+JzwIOS/oLS3v9vZfEe4KWy5+3PYmMW9wULFjA4OFhnKmZm7UnSC9Ueq/eE6meAz0fEfODzwG11JLUuG68fPHjwYJ1pmJlZJfUW948D92a3/wpYnt0eAuaXPW9eFjtBRNwSEX0R0dfdXfGowszM6lRvcX8Z+O3s9kXAT7Lb9wFXZrNmzgdeG2+83czM8jfumLukbcBKYI6k/cBG4Crga5JOAt6gNDMG4AHgUmAf8DrwiSnI2czMxjGR2TJrqzz0gQrPDeDqySZlZmaTU+9sGatiYPcQmx7cy8uHjzC3q5NrVi2mf2lP0WmZWZtxcc/RwO4h1t+7hyNvHQNg6PAR1t+7B8AF3swayr1lcrTpwb3vFvZhR946xqYH9xaUkZm1Kxf3HL18+EhNcTOzqeLinqO5XZ01xc3MpoqLe46uWbWYzukdI2Kd0zu4ZtXigjIys3bl4p6j/qU9LOudNSK2rHeWT6aaWcO5uOdow8Aedjx7aERsx7OH2DCwp6CMzKxdubjnaNujL9UUNzObKi7uOToWUVPczGyquLjnqEOqKW5mNlVc3HO09rz5NcXNzKaK2w/k6Pr+JUBpjP1YBB0Sa8+b/27czKxRFAmMB/f19YUvs2dmVhtJuyKir9JjHpYxM2tBHpZpI25HbNY+XNzbhNsRm7UXD8u0CbcjNmsvLu5twu2IzdqLi3ubcDtis/YybnGXtFnSAUlPlsXukvR49vO8pMfLHlsvaZ+kvZJWTVXiVhu3IzZrLxM5oboF+AZw+3AgIn5/+LakvwRey26fBawBzgbmAt+XdGZEjBzsbWEbBvYkuYipf2kPgy8cGpHb5R/o8clUsxY17p57RGwHDlV6TJKAfwVsy0KrgTsj4mhEPAfsA5bnlGvyNgzs4Y6dL77bKOxYBHfsfDGJlr8Du4e4Z9fQiNzu2TXEwO6hgjMzs6kw2TH3DwGvRsRPsvs9QHl/2/1ZrC2k3PLXs2XM2stki/taju+110TSOkmDkgYPHjw4yTTSkHLLX8+WMWsvdRd3SScBlwF3lYWHgPIWiPOy2Aki4paI6IuIvu7u7nrTSErKLX89W8asvUxmz/2fAc9ExP6y2H3AGkkzJJ0BLAIem0yCzSTllr+eLWPWXsadLSNpG7ASmCNpP7AxIm6jNCtmxJBMRDwl6W7gaeBt4Op2mimTcsvf4Vkx7i1j1h7c8tfMrEmN1fLXjcPMmoy7e9pEuLibNRF397SJcm8Zsybi9Qo2Ud5zz5kPmWvnbTZxXq9gE+XiniMfMtfO26w2c7s6GapQyL1ewUbzsEyOfMhcO2+z2ni9gk2U99xzlPohc4odK1PfZqnxegWbKBf3HM08uYNfvXnimq2ZJ3dUeHZjDXesHDbcsRIotMB3zZzOz15/q2LcKutf6lbNNj4Py+To9QqFfax4I6XasbLaGroE1taZNTUX9xxVq0cp1KlUO1a+duTEvfax4mY2MS7uOUq5K2SqublbpdnUcHHPUcpdIVPNzbM/zKaGT6jmKOWukKnm5tkfZlPDXSHNzJrUWF0hPSxjZtaCXNzNzFqQi7uZWQtycTcza0Eu7mZmLcjF3cysBY1b3CVtlnRA0pOj4v9G0jOSnpL052Xx9ZL2SdoradVUJG1mZmObyCKmLcA3gNuHA5IuBFYDvxkRRyWdlsXPAtYAZwNzge9LOjMiiu+c1SC+qlDtvM3M8jfunntEbAcOjQp/BrgpIo5mzzmQxVcDd0bE0Yh4DtgHLM8x36QNX1Vo6PARguNXFRrYPVR0asnyNjObGvWOuZ8JfEjSo5L+j6QPZvEeoLyH7P4s1hZ8VaHaeZuZTY16e8ucBMwGzgc+CNwt6R/W8gckrQPWAfT29tacQIqH8r6qUO28zcymRr177vuBe6PkMeAdYA4wBJS3GZyXxU4QEbdERF9E9HV3d9f04qkeyrt9be28zcymRr3FfQC4EEDSmcDJwE+B+4A1kmZIOgNYBDyWR6LlUj2Ud/va2nmbmU2NcYdlJG0DVgJzJO0HNgKbgc3Z9Mg3gY9Hqb3kU5LuBp4G3gaunoqZMqkeyrt9be28zcymRlO2/F1x08MMVSjkPV2d7Lj2ojxTMzNLVsu1/PWhvJnZ2JrySkw+lDczG1tTFncoFXgXczOzyppyWMbMzMbm4m5m1oJc3M3MWlDTjrlb7a649RF2PHu8B9yKhbPZetUFBWZkZlPFe+5tYnRhB9jx7CGuuPWRgjIys6nk4t4mRhf28eJm1txc3M3MWpCLu5lZC3JxbxMrFs6uKW5mzc3FvU1sveqCEwq5Z8uYtS5PhWwjLuRm7cN77mZmLch77jlL8dquZtZ+XNxzNHxt1+FLAA5f2xVwgTezhvKwTI5SvbarmbUfF/ccpXptVzNrPy7uOZrb1VlT3Mxsqoxb3CVtlnRA0pNlsX8vaUjS49nPpWWPrZe0T9JeSaumKvEU+dquZpaKiZxQ3QJ8A7h9VPyrEfEX5QFJZwFrgLOBucD3JZ0ZEcdoA762q5mlYtziHhHbJS2Y4N9bDdwZEUeB5yTtA5YDbdNX1td2NbMUTGbM/bOSnsiGbf5+FusBXip7zv4sZmZmDVRvcb8ZWAicC7wC/GWtf0DSOkmDkgYPHjxYZxpmZlZJXcU9Il6NiGMR8Q5wK6WhF4AhYH7ZU+dlsUp/45aI6IuIvu7u7nrSMDOzKuoq7pJOL7v7L4HhmTT3AWskzZB0BrAIeGxyKZqZWa3GPaEqaRuwEpgjaT+wEVgp6VwggOeBfw0QEU9Juht4GngbuLpdZsqYmaVEEVF0DvT19cXg4GDRaZiZNRVJuyKir9JjXqFqZtaCXNzNzFqQi7uZWQtycTcza0Eu7mZmLcjF3cysBfkyezlL+RqqF3/lb/jJgV+9e3/Rae/hoS+sLC4hM5sy3nPP0fA1VIcOHyE4fg3Vgd0VOzA01OjCDvCTA7/i4q/8TTEJmdmUcnHPUcrXUB1d2MeLm1lzc3HPka+hamapcHHPka+hamapcHHPUcrXUF102ntqiptZc3Nxz1H/0h5uvGwJPV2dCOjp6uTGy5YkMVvmoS+sPKGQe7aMWetyV0gzsyblrpBmZm3Gi5jaSMoLrMwsXy7ubWJ4gdXwPPzhBVaAC7xZC/KwTJtIeYGVmeXPxb1NeIGVWXtxcW8TXmBl1l7GLe6SNks6IOnJCo/9W0khaU52X5K+LmmfpCckLZuKpK12KS+wMrP8TWTPfQtwyeigpPnAPwdeLAt/GFiU/awDbp58ipaH/qU9LOudNSK2rHeWT6aatahxi3tEbAcOVXjoq8AXgfJVUKuB26NkJ9Al6fRcMrVJ2TCwhx3Pjnwbdzx7iA0DewrKyMymUl1j7pJWA0MR8cNRD/UAL5Xd35/FrGDbHn2ppriZNbea57lLmgn8O0pDMnWTtI7S0A29vb2T+VM2AceqtJmoFm8kL64yy189e+4LgTOAH0p6HpgH/EDSe4EhYH7Zc+dlsRNExC0R0RcRfd3d3XWkYbVQjfFGSfnqVWbNrObiHhF7IuK0iFgQEQsoDb0si4j/B9wHXJnNmjkfeC0iXsk3ZavHzJM7aoo3ihdXmU2NiUyF3AY8AiyWtF/Sp8Z4+gPA/wX2AbcCf5xLljZpr795rKZ4o3hxldnUGHfMPSLWjvP4grLbAVw9+bQsb3O7OhmqUDCLXsSUal5mzc4rVNtEqouYUs3LrNm5K2SbGJ59ktqslFTzMmt2vhKTmVmT8pWYzMzajIu7mVkLcnE3M2tBPqHaRjYM7GHboy9xLIIOibXnzef6/iVFp5VsXqny9rKJcHFvExsG9nDHzuPdmY9FvHu/yMKQal6p8vayifKwTJtItStkqnmlytvLJsrFvU2k2hUy1bxS5e1lE+Xi3iY6VLn/Y7V4o6SaV6q8vWyiXNzbxNrz5tcUb5RU80qVt5dNlE+otonhk22pzbJINa9UeXvZRLn9gJlZk3L7ATOzNuPibmbWglzczcxakIu7mVkLcnE3M2tBLu5mZi3Ixd3MrAWNu4hJ0mbgI8CBiHh/FvsPwGrgHeAA8IcR8bIkAV8DLgVez+I/mKrkrTW4ha1Z/iay574FuGRUbFNEnBMR5wL3A3+WxT8MLMp+1gE355SntajhFrbDja+GW9huGNhTcGZmzW3c4h4R24FDo2I/L7v7HmB4metq4PYo2Ql0STo9r2St9biFrdnUqLu3jKQbgCuB14ALs3APUP6t3J/FXqnw++so7d3T29tbbxrW5NzC1mxq1H1CNSKui4j5wFbgs3X8/i0R0RcRfd3d3fWmYU3OLWzNpkYes2W2Apdnt4eA8t6j87KYWUVuYWs2Neoq7pIWld1dDTyT3b4PuFIl5wOvRcQJQzJmw67vX8LHzu99d0+9Q+Jj5/d6tozZJE1kKuQ2YCUwR9J+YCNwqaTFlKZCvgB8Onv6A5SmQe6jNBXyE1OQs7WY6/uXuJib5Wzc4h4RayuEb6vy3ACunmxSZmY2OV6hambWglzczcxakIu7mVkLcnE3M2tBLu5mZi2o7vYDVtnA7iE2PbiXlw8fYW5XJ9esWkz/0p6i00paql0hU30vU83LanPFrY+w49njbbtWLJzN1qsuyO3ve889RwO7h1h/7x6GDh8hgKHDR1h/7x4GdnuRbjWpdoVM9b1MNS+rzejCDrDj2UNccesjub2Gi3uONj24lyNvHRsRO/LWMTY9uLegjNKXalfIVN/LVPOy2owu7OPF6+HinqOXDx+pKW7pdoVM9b1MNS9Lj4t7juZ2ddYUt3S7Qqb6Xqaal6XHxT1H16xaTOf0jhGxzukdXLNqcUEZpS/VrpCpvpep5mW1WbFwdk3xeri456h/aQ83XraEnq5OBPR0dXLjZUs8k2EMqXaFTPW97F/aw+Uf6BmxvS7/QE/heUHpZO+Kmx7mjGu/w4qbHvZJ3jFsveqCEwp53rNlFAlc8aavry8GBweLTsMsecOzZcpPqnZO7yj8P55U82p1knZFRF+lx7znbtZEUp0tk2pe7cyLmKxwXpQzcanOlkk1L0j38zXVebm4W6FGH84PL8oBkvgCpmZuVydDFQpm0bNlUs0r1c9XI/LysIwVyofztUl1tkyqeaX6+WpEXt5zt0L5cL42w6+fYl6DLxwa0SMohVk8qX6+GpGXi7sValbndA4feativEipHs4Pv37ROYw2sHuIe3YNjegRdM+uIfp+Y3ahuaY6XNSIz72HZaxQ1RaiFrxANdnD+VSlur1SHS5qxOd+3D13SZuBjwAHIuL9WWwT8C+AN4FngU9ExOHssfXAp4BjwJ9ExIP5pWut5vDrJ+69jBVvlFQP5wHO2fg9fn70eCE9dUYHT3zpkgIzSnd79S/t4a8GXxzRkGtZ76zCj3wa8bmfyJ77FmD0J+ch4P0RcQ7wY2A9gKSzgDXA2dnvfFNSB2ZVVDsMLXpYpmtm5devFm+U0YUd4OdHj3HOxu8VlFHaNgzsqdhat+iW0o3oETRucY+I7cChUbH/GRFvZ3d3AvOy26uBOyPiaEQ8B+wDlueWrbWcVIdlqi3cLnpB9+jCPl68UaptlqLXv6faUroRw0V5jLl/EvhudrsHKN9q+7PYCSStkzQoafDgwYM5pGHNKNVhmdcqnOwaK25pSrWldCN6BE2quEu6Dngb2Frr70bELRHRFxF93d3dk0nDmliqLWxTzctqk2pL6Wqzi/JstlZ3cZf0h5ROtF4Rx7uPDQHlvVrnZTGzilKdzZBqXqfOqHwKq1q8UVLNK9WW0o2YXVRXcZd0CfBF4Hcj4vWyh+4D1kiaIekMYBHw2OTTtFbl1rq1eeJLl3BKx8i9zlM6VPhsmVTzSrWldBKLmCRtA1YCcyTtBzZSmh0zA3hIpY22MyI+HRFPSbobeJrScM3VEVHsmR5Lnhfl1JaXpk2DY8e/Vpo2jYHdQ86riuv7lxRezEdrxOKqicyWWRsRp0fE9IiYFxG3RcT7ImJ+RJyb/Xy67Pk3RMTCiFgcEd8d62+bpSrVRTnOqzU0YtjP7QfMKkh1UY7zag2N6BHk4m5WQTv3JKlHqtsrZVM9HOneMmYVpDpbJtVFX6lur3bm4m5WQaqzZVJd9JXqrKd25mEZswpSnS2T8vBHirOe2pmLu1kFY83+KLKAXbNqMZ+76/GK8aKleHETKDUPK7+IyNrz5icxNfKKWx8Z0dRsxcLZbL3qgtz+vodlzCpIdfbHjQ88XVO8UYYvbjJ0+AjB8Yub5Lmcvh4bBvZwx84XRxyB3bHzxcK7Qo4u7FDqVnnFrY/k9hou7mYVpNpb5tVfvFlTvFFSneeealfI0YV9vHg9XNzNKvDsj9qkeqSTalfIRvCYu1kFqV6IOlWpzr/vkCoW8qK7QjaCi7tZFSnO/pgmeKfCTue0gmtVqvPv1543nzt2vlgxXqQVC2dXHIJZsXB2bq/hYRmzJpLqFaJSnX+falfIrVddwK//2skjYr/+ayfnOlvGe+5mTWTmyR386s0TG63OPLnYvuldM6fzswqFvOhrzkKaXSE3DOw54ST4q794kw0De3LL1XvuZk3k9QqFfax4o6R6RJGqRszi8Z57zlJdyGGtIdULUad8zdkUv5ONmMXj4p6j4YUcw/N9hxdyAIV/mKw1iMqFvOi5HydNg7feqRwvUjt/Jz0sk6NUF3JY60h1z71SYR8r3iipfier/Wec53/SLu45SnUhh1m7SvU72Yj/pF3cc5TqknVrHY3Y42slqX4nqy2iynNxlYt7jrxk3aZatSmPRU+FrLb4Js9FOfVI9TtZbRFVnourxi3ukjZLOiDpybLY70l6StI7kvpGPX+9pH2S9kpalVumTSD1CxYM7B5ixU0Pc8a132HFTQ8X3rHPapfqVMhGLMqpR//SHpb1zhoRW9Y7q/Dv5PX9SzilY+Re+ikdynU+/kT23LcAl4yKPQlcBmwvD0o6C1gDnJ39zjclFbtL0WD9S3vYce1FPHfT77Dj2osK/xANS7Ulq9Um1T33sRblFGnDwJ6KrXWLzuu8Gx7ijWMjR9jfOBacd8NDub3GuMU9IrYDh0bFfhQRlU43rwbujIijEfEcsA9YnkumNimpzhqw2qS6555qa91U82pE6+a857n3ADvL7u/PYieQtA5YB9Db25tzGjZaqrMGUpbi4pdUp0Km2lo31bwaobATqhFxS0T0RURfd3d3UWm0jVRnDaQq1WGsRsyyqIfzSk/exX0IKD/dOy+LWcFSnTWQqlSHsRoxy6Iezqs2o08+jxevR97F/T5gjaQZks4AFgGP5fwaVofUZ/KkJtVhrFRb2Dqv2jx63cUVZxc9et3Fub2GYpyxJ0nbgJXAHOBVYCOlE6z/CegGDgOPR8Sq7PnXAZ8E3gY+FxHfHS+Jvr6+GBwcrP9fYZazFTc9zFCFQt7T1cmOay8qICOzE0naFRF9lR4b94RqRKyt8tBfV3n+DcANE0/PLD3XrFo8ouEUeBjLmou7QppV4GuoWrNzcTerIsVrqJpNlHvLmJm1IBd3M7MW5OJuZtaCXNzNzFqQi7uZWQsadxFTQ5KQDgIv1Pnrc4Cf5phOXlLNC9LNzXnVxnnVphXz+o2IqNicK4niPhmSBqut0CpSqnlBurk5r9o4r9q0W14eljEza0Eu7mZmLagVivstRSdQRap5Qbq5Oa/aOK/atFVeTT/mbmZmJ2qFPXczMxulqYq7pFMkPSbph5KekvSlUY9/XdIvU8lL0hZJz0l6PPs5N5G8JOkGST+W9CNJf5JIXn9btq1eljSQSF7/VNIPsrz+TtL7EsnroiyvJyV9W1IhjQAldUjaLen+7P4Zkh6VtE/SXZLyu7zQ5PL6bJZTSJpTRE5V8toqaW/2Pm6WND2XF4qIpvkBBPy97PZ04FHg/Ox+H/BfgV+mkhewBfhoatsL+ARwOzAte+y0FPIa9Zx7gCtTyAv4MfCPs/gfA1sSyOu3gJeAM7P4l4FPFfQ5+wLw34D7s/t3A2uy298CPpNIXkuBBcDzwJwicqqS16XZeyxgW17bq6n23KNkeM98evYTkjqATcAXU8qriFzKjZHXZ4AvR8Q72fMOJJIXAJJOBS4CGrrnPkZeAZyaxWcBLyeQ1zHgzYj4cRZ/CLi8kXkBSJoH/A7wn7P7ovTe/ffsKd8G+ovOCyAidkfE843OpVyVvB7I3uOgdFnSeXm8VlMVd3j3kOZx4ADwUEQ8CnwWuC8iXkksL4AbJD0h6auSZiSS10Lg9yUNSvqupEWJ5DWsH/hfEfHzRPL6I+ABSfuBPwBuKjovSkXgJEnDi18+ysiL0zfKf6S0U/VOdv8fAIcj4u3s/n6giKb4o/NKRdW8suGYPwC+l8cLNV1xj4hjEXEupf/dlkv6J8DvUbqma0p5vR9YD/wj4IPAbOBPE8lrBvBGlFbF3QpsTiSvYWspHZ42XJW8Pg9cGhHzgP8CfKXovICzgTXAVyU9BvyC0t58w0j6CHAgInY18nXH08R5fRPYHhF/m8frNV1xHxYRh4H/DVwIvA/YJ+l5YKakfQnkdUlEvJIdbR2lVBSWp5AXpb2pe7OH/ho4J5G8yE50LQe+U1ROo/L6MPCbZUcWd1Ea7y46r0si4pGI+FBELAe2Uzo30EgrgN/Nvnd3UhqO+RrQVXZydx4wVHReku5ocA6VVM1L0kagm9J4fC6aqrhL6pbUld3uBC4GdkXEeyNiQUQsAF6PiEbPZqiU1zOSTs9iojTU8GQKeVEay74we9pv0+CiMEZeUBpeuD8i3mhkTmPk9SNglqQzs6cNx4rO6xlJp2WxGZSOCr/VyLwiYn1EzMu+d2uAhyPiCkr/+Xw0e9rHgf+RQF4fa2QOlVTLS9IfAauAtcPnwfLQbNdQPR34dnYCdRpwd0TcX3BOUCUvSQ9L6qZ0Fvxx4NOJ5PV3wFZJnwd+SWlMufC8ssfWUMCY9lh5SboKuEfSO8DPgE8mktem7FB/GnBzRDzc4Lyq+VPgTknXA7uB2wrOBwCVpvx+EXgv8ISkByKi0Z/9Sr5FqSvuI6X9QO6NiC9P9o96haqZWQtqqmEZMzObGBd3M7MW5OJuZtaCXNzNzFqQi7uZWQtycTcza0Eu7mZmLcjF3cysBf1/bnFCN+xGIOYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(data['Temp'], data['Snacks'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hNnyGby3dH9h",
        "outputId": "3f6ebafc-7b6c-401a-b2f2-6c6e5af74da7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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uVPfjxsvP2K+sU7gKJdX9mOrjHhqfrdREHklnAF8pLiP8a+BaYC/wB+CzEfHUgb7eE3nMzMobaiJPzdeBA0TEJmBT8fEGYEM9wpmZWXl+JaaZWaZc4GZmmXKBm5llygVuZpYpF7iZWaZc4GZmmXKBm5llygVuZpYpF7iZWaZKvRLT3pLqBJBUc6U6+SbVXKnux1RzpbofobHTlXwEPgypTgBJNVeqk29SzZXqfkw1V6r7ERo/XckFPgypTgBJNVeqk29SzZXqfkw1V6r7ERo/XckFPgypTgBJNVeqk29SzZXqfkw1V6r7sRlc4MOQ6gSQVHOlOvkm1Vyp7sdUc6W6H5vBBT4MqU4ASTVXqpNvUs2V6n5MNVeq+xEaP13JBT4MqU4ASTVXqpNvUs2V6n5MNVeq+xEaP12p1ESekfJEHjOz8oaayOMjcDOzTLnAzcwy5QI3M8uUC9zMLFMucDOzTLnAzcwy5QI3M8uUC9zMLFMucDOzTLnAzcwyVfNEHkltQBfQHRHnSxKwAvgY0At8KyK+We+AqU7aSDVXqlLdXp4wMzqkvL0ama3MSLUvAQ8Dhxe3Pw1MB46LiL2SptQlUT99kzb63qy9b9IG0NKdk2quVKW6vfomzPTpmzADtLTEU91eqUp5ezU6W02nUCRNAz4CfKff8qXAtRGxFyAiekacZoBUJ22kmitVqW4vT5gZHVLeXo3OVus58G8AVwJ7+63NAj4hqUvSjyUdPdgXSlpa3Kdr165dpcKlOmkj1VypSnV7ecLM6JDy9mp0tqoFLul8oCcitg341Djg1eItDtcANwz29RGxOiI6IqKjvb29VLhUJ22kmitVqW4vT5gZHVLeXo3OVssR+DzgAkk7gVuAMyWtBZ4F1hf32QAcX5dE/aQ6aSPVXKlKdXt5wszokPL2anS2qr/EjIhlwDIASWcAX4mIJZK+BswHfgV8CHisLon66TvJn9pvl1PNlapUt1ffLypTuwol1e2VqpS3V6OzlZrI06/Az5c0EVgHzABeBr4QEQ8c6Os9kcfMrLyhJvKUuYyQiNgEbCo+3k3lyhQzM2sBvxLTzCxTLnAzs0y5wM3MMuUCNzPLlAvczCxTLnAzs0y5wM3MMuUCNzPLlAvczCxTpV6JaW9JdZKLjQ6pTphJNddY5QIfhlQnudjokOqEmVRzjWU+hTIMqU5ysdEh1QkzqeYay1zgw5DqJBcbHVKdMJNqrrHMBT4MqU5ysdEh1QkzqeYay1zgw5DqJBcbHVKdMJNqrrHMv8QchlQnudjokOqEmVRzjWWlJvKMlCfymJmVN9REHp9CMTPLlAvczCxTLnAzs0y5wM3MMuUCNzPLVFOvQpG0C/j1ML98MvB8HePUi3OV41zlOFc5qeaCkWX784hoH7jY1AIfCUldg11G02rOVY5zleNc5aSaCxqTzadQzMwy5QI3M8tUTgW+utUBhuBc5ThXOc5VTqq5oAHZsjkHbmZmb5fTEbiZmfXjAjczy1SSBS7pHZLuk/SApP+TdM2Az39T0sup5JL0PUm/knR/8efERHJJ0kpJj0l6WNI/JJLrZ/221XOSOhPJ9WFJvyxy/VzSexPJdWaR6yFJN0pqydtAS2qTtF3S7cXtd0vaKukJSbdKOjSRXF8sMoWkya3INESudZIeLfbjDZIOGfEPiYjk/gAC/rT4+BBgKzC3uN0B/Bfwciq5gO8BH01tewGfAf4TOKj43JQUcg24z/eBv00hF/AY8BfF+t8B30sg1weBZ4BjivVrgc+16HF2OXATcHtx+zbg4uLjbwOXJpLrJGAmsBOY3IpMQ+Q6r9jHAm6ux/ZK8gg8KvqOsA8p/oSkNmAVcGVKuVqRpb8D5LoUuDYi9hb360kkFwCSDgfOBJp6BH6AXAEcXqwfATyXQK5e4PWIeKxY3wj8TTNzAUiaBnwE+E5xW1T23X8Xd7kRWNjqXAARsT0idjY7S39D5Lqj2McB3AdMG+nPSbLAYd9/P+4HeoCNEbEV+CLww4j4TWK5AFZKelDS1yWNSyTXLOATkrok/VjS0Ynk6rMQ+ElEvJhIrs8Dd0h6Fvgk8LVW56LyRD9YUt8r+D4KtGJ23zeoHDjtLW7/GbA7It4sbj8LtGI0z8BcqRgyV3Hq5JPAnSP9IckWeET0RsSJVP6VOkXS6cDHgH9LLNf7gWXAccAHgEnAVxPJNQ54NSov310D3JBIrj6LqPxXsumGyPVl4LyImAZ8F7i+1bmA9wEXA1+XdB/wEpWj8qaRdD7QExHbmvlzq8k4178DP42In430ZyVb4H0iYjfwP8B84L3AE5J2An8i6YkEcp0TEb8p/mf0GpUn/ikp5KJyVLS++NQG4PhEclH8cukU4EetyjQg17nACf3+h3ArlfPPrc51TkRsjoi/jIhTgJ9SOVffTPOAC4rn3S1UTp38KzCx3y9UpwHdrc4laW2TMwxmyFySrgbaqZwfH7EkC1xSu6SJxcfjgQXAtoh4Z0TMjIiZwB8jotlXCQyW6xFJ7yrWROW0wEMp5KJybnl+cbcP0eQn/gFyQeVUwO0R8WozMx0g18PAEZKOKe7Wt9bqXI9ImlKsjaPyv7tvNzNXRCyLiGnF8+5i4J6IWEzlH5iPFnf7FPCDBHItaWaGwQyVS9LngbOBRX2/lxqpVKfSvwu4sfil5UHAbRFxe4szwRC5JN0jqZ3Kb5fvB76QSK6fA+skfRl4mco53pbnKj53MS04x3ygXJIuAb4vaS/wB+CzieRaVfy3/CDgWxFxT5NzDeWrwC2SVgDbgf9ocR4AVLlc9krgncCDku6IiGY/9gfzbSpvp725cqzH+oi4diTf0C+lNzPLVJKnUMzMrDoXuJlZplzgZmaZcoGbmWXKBW5mlikXuJlZplzgZmaZ+n+G5avVIlcY3wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ref: https://myanmardatascience.com/"
      ],
      "metadata": {
        "id": "kNUKVXo9ZZDp"
      }
    }
  ]
}