Waiter's_tips_analysis_and_prediction.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/VaishnaviMudaliar/Waiter-s-tips-analysis-and-prediction/blob/main/Waiter's_tips_analysis_and_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "#Importing libraries "
      ],
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        "id": "0VOIP8yjfkh-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CB4H5RkaUesJ"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing the dataset"
      ],
      "metadata": {
        "id": "5mcF-LmKfrzP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualizing the dataset"
      ],
      "metadata": {
        "id": "oRiux3jwiNFl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/tips.csv\")\n",
        "dataset.head()"
      ],
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          "height": 206
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        "outputId": "97406289-b42d-46b2-983f-9cecda883fee"
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      "execution_count": 36,
      "outputs": [
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          "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"
            ],
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              "      <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",
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              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
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              "      <td>No</td>\n",
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              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
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              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
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              "    <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",
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    {
      "cell_type": "markdown",
      "source": [
        "##Analysing the tip according to the day(Thursday,Friday,Saturday or Sunday)"
      ],
      "metadata": {
        "id": "ykP5TTfjfwR6"
      }
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      "cell_type": "code",
      "source": [
        "figure = px.scatter(data_frame = dataset,x = \"total_bill\",y = \"tip\",size = \"size\" , color = \"day\" , trendline =\"ols\")\n",
        "figure.show()"
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    {
      "cell_type": "markdown",
      "source": [
        "##Analysing the tip according to the gender(Male or Female"
      ],
      "metadata": {
        "id": "sTXqapyGgDoU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter(data_frame = dataset,x = \"total_bill\",y = \"tip\",size = \"size\" , color = \"sex\" , trendline =\"ols\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "l7XrEQDeY3o5",
        "outputId": "9166f370-d09d-4d69-d4d8-43253cc191bd"
      },
      "execution_count": 11,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "##Analyzing tip according to the time of the day(Lunch or Dinner)"
      ],
      "metadata": {
        "id": "k5fJxljNgUmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter(data_frame = dataset,x = \"total_bill\",y = \"tip\",size = \"size\" , color = \"time\" , trendline =\"ols\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "GogkPo1kZGuC",
        "outputId": "57362ead-ee71-4946-a892-fbed10c72f24"
      },
      "execution_count": 12,
      "outputs": [
        {
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "figure = px.pie(dataset,values = 'tip' , names = \"day\",hole = 0.5)\n",
        "figure.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WuYYoGVdZMyu",
        "outputId": "28784db7-2b9e-4fc6-8c3b-1bc93ca2a482"
      },
      "execution_count": 13,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "35% of customers pay tips on Saturdays"
      ],
      "metadata": {
        "id": "FXQJSEUognL_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure = px.pie(dataset,values = 'tip' , names = \"sex\",hole = 0.5)\n",
        "figure.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "2wH85jryZpPX",
        "outputId": "3d8cd5d7-60c0-4185-b4d4-1362f696d147"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "66.3% of tip paying customers are Male"
      ],
      "metadata": {
        "id": "_AyZNZWHg2i4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure = px.pie(dataset,values = 'tip' , names = \"smoker\",hole = 0.5)\n",
        "figure.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "NsFQeQllZ5dW",
        "outputId": "4866e503-d88e-4ec1-8dd5-19058b5bf7a8"
      },
      "execution_count": 16,
      "outputs": [
        {
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          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "61.8% of the tip paying customers are non-smokers"
      ],
      "metadata": {
        "id": "ZuCinoj_g-Wv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure = px.pie(dataset,values = 'tip' , names = \"time\",hole = 0.5)\n",
        "figure.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "lhZwFgAEaGJe",
        "outputId": "2dcfaeb7-1575-4e8b-cf7e-c0791cf523be"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "74.6% of the customers pay tips for dinner"
      ],
      "metadata": {
        "id": "c5EdGpfWhIzj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Label encoding \n",
        "Label encoding is a preprocessing step carried out to convert the categorical values to numerical values in order to feed the data to the machine learning algorithm."
      ],
      "metadata": {
        "id": "dtyy2VSbhRYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "dataset['sex'] = label_encoder.fit_transform(dataset['sex'])\n",
        "\n",
        "dataset['day'] = label_encoder.fit_transform(dataset['day'])\n",
        "\n",
        "dataset['time'] = label_encoder.fit_transform(dataset['time'])\n",
        "\n",
        "dataset['smoker'] = label_encoder.fit_transform(dataset['smoker'])\n",
        "\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RBb2FxClbqA8",
        "outputId": "7ec3ac40-bf98-44ca-a471-31fa54762408"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip  sex  smoker  day  time  size\n",
              "0       16.99  1.01    0       0    2     0     2\n",
              "1       10.34  1.66    1       0    2     0     3\n",
              "2       21.01  3.50    1       0    2     0     3\n",
              "3       23.68  3.31    1       0    2     0     2\n",
              "4       24.59  3.61    0       0    2     0     4"
            ],
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              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
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              "      <th>size</th>\n",
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              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
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              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
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              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5e91a53a-7e63-4834-8e29-6865b1f6843c 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-5e91a53a-7e63-4834-8e29-6865b1f6843c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array(dataset[['total_bill','sex','smoker','day','time','size']])\n",
        "y = np.array(dataset['tip'])\n",
        "\n"
      ],
      "metadata": {
        "id": "SxK3Afc3dr84"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the dataset"
      ],
      "metadata": {
        "id": "v8isusdyhwcH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size = 0.2,random_state = 42)\n"
      ],
      "metadata": {
        "id": "yjEdDZVbeZaN"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training the Linear Regression Model"
      ],
      "metadata": {
        "id": "Lj114BdNh7Sf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(xtrain, ytrain)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vctZSt-7exuC",
        "outputId": "e4279cbb-e293-4664-81cb-f4dbe313946d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prediction step"
      ],
      "metadata": {
        "id": "84rVHZp-iA6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# features = [[total_bill, \"sex\", \"smoker\", \"day\", \"time\", \"size\"]]\n",
        "features = np.array([[24.50, 1, 0, 0, 1, 4]])\n",
        "model.predict(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AJ0knAwfW0R",
        "outputId": "add9a445-c675-4128-e8cd-c7c08fba1d20"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.97416925])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    }
  ]
}