Uni_Bi_Multivariate_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/chemutai254/0x01-NoSQL/blob/master/Uni_Bi_Multivariate_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 2,
      "metadata": {
        "id": "I3MkUnNPnYsE"
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      "source": [
        "# Data pre-processing - Pandas\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data visaulization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "-4vxyv2tqekA"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the datasets\n",
        "data = sns.load_dataset(\"tips\")"
      ],
      "metadata": {
        "id": "c7MQcABlqwiK"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View the top 5 rows\n",
        "data.head()"
      ],
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      "execution_count": 4,
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              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
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              "      <th>total_bill</th>\n",
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              "      <th>smoker</th>\n",
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              "      <th>size</th>\n",
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              "      <td>16.99</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
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      "cell_type": "code",
      "source": [
        "# Inspect the dataset\n",
        "data.info()"
      ],
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      "execution_count": 5,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 244 entries, 0 to 243\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   total_bill  244 non-null    float64 \n",
            " 1   tip         244 non-null    float64 \n",
            " 2   sex         244 non-null    category\n",
            " 3   smoker      244 non-null    category\n",
            " 4   day         244 non-null    category\n",
            " 5   time        244 non-null    category\n",
            " 6   size        244 non-null    int64   \n",
            "dtypes: category(4), float64(2), int64(1)\n",
            "memory usage: 7.4 KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Statistical Analysis\n",
        "data.describe()"
      ],
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      "execution_count": 6,
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          "data": {
            "text/plain": [
              "       total_bill         tip        size\n",
              "count  244.000000  244.000000  244.000000\n",
              "mean    19.785943    2.998279    2.569672\n",
              "std      8.902412    1.383638    0.951100\n",
              "min      3.070000    1.000000    1.000000\n",
              "25%     13.347500    2.000000    2.000000\n",
              "50%     17.795000    2.900000    2.000000\n",
              "75%     24.127500    3.562500    3.000000\n",
              "max     50.810000   10.000000    6.000000"
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              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.785943</td>\n",
              "      <td>2.998279</td>\n",
              "      <td>2.569672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.902412</td>\n",
              "      <td>1.383638</td>\n",
              "      <td>0.951100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.070000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.347500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.795000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.127500</td>\n",
              "      <td>3.562500</td>\n",
              "      <td>3.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.810000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
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      "source": [
        "EXPLORATORY DATA ANALYSIS\n",
        "1. Univariate Analysis\n",
        "2. Bivariate Analysis\n",
        "3. Multivariate Analysis"
      ],
      "metadata": {
        "id": "hVmaN-A7uGhy"
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    {
      "cell_type": "markdown",
      "source": [
        "Univariate Analysis - Analysis based on single features (Columns) in the dataset."
      ],
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        "id": "Wk2Cq3gHvLM_"
      }
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      "cell_type": "code",
      "source": [
        "data.columns"
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      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Customers by Sex\n",
        "plt.figure(figsize=(5,3)) # resize the figure\n",
        "sns.countplot(x=data['sex'])"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Customers by Smoker\n",
        "plt.figure(figsize=(5,3))\n",
        "sns.countplot(x=data['smoker'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "sa_tO4TGw795",
        "outputId": "0c325039-87bc-4cb1-8c3e-2bbacf19d9c2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='smoker', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Customers by day\n",
        "plt.figure(figsize=(5,3))\n",
        "sns.countplot(y=data['day'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "LtmW0QHFxd51",
        "outputId": "f3a29c8f-1e37-4b1e-ab33-1e78f6852115"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='day'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualiza the numerical columns - Total Bills and Tips (BoxPlots)\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,3))\n",
        "sns.boxplot(data['total_bill'], ax=axes[0])\n",
        "axes[0].set_title(\"Total bills\")\n",
        "sns.boxplot(data['tip'], ax=axes[1])\n",
        "axes[1].set_title(\"Tips\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "kQ6-HKtQxOIE",
        "outputId": "ee323ed5-0fb1-45dd-d200-b1762224c024"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Tips')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bivariate Analysis - Analysis based on two features (Columns) in the dataset"
      ],
      "metadata": {
        "id": "etRaFivr0wA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LMOpQyb11H-a",
        "outputId": "55e8413c-5646-4c92-989c-5ba199ce1bed"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3913e3e3-5e7c-41ac-b04f-de277403ed83\">\n",
              "    <div 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",
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              "        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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3913e3e3-5e7c-41ac-b04f-de277403ed83')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total bills by sex and Total Tips by sex\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,3))\n",
        "\n",
        "total_billSex = data.groupby('sex')['total_bill'].sum().reset_index()\n",
        "sns.barplot(x='sex', y='total_bill', data=total_billSex, ax=axes[0])\n",
        "axes[0].set_title('Total Bill by sex')\n",
        "\n",
        "total_tipSex = data.groupby('sex')['tip'].sum().reset_index()\n",
        "sns.barplot(x='sex', y='tip', data=total_tipSex, ax=axes[1])\n",
        "axes[1].set_title('Total Tip by sex')\n",
        "\n",
        "total_billSex\n",
        "total_tipSex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "RyQWJsXU05TZ",
        "outputId": "0c2e0589-7ad0-434f-b26d-2a4635381058"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex     tip\n",
              "0    Male  485.07\n",
              "1  Female  246.51"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
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              "      <td>485.07</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>246.51</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27cac07d-bbf0-4edb-949d-5abc1b45b090')\"\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-27cac07d-bbf0-4edb-949d-5abc1b45b090 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-27cac07d-bbf0-4edb-949d-5abc1b45b090');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 2 Axes>"
            ],
            "image/png": 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    },
    {
      "cell_type": "code",
      "source": [
        "# Is there a relationship between Total bill and tip\n",
        "sns.scatterplot(x='total_bill', y='tip', data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "937UtPKs1ZQ1",
        "outputId": "01a050d7-3e54-47ef-cdba-201803008ce4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='total_bill', ylabel='tip'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nqoznbNh1ZE2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}