ML/AssociationRuleMining(Apriori_algorithm).ipynb

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        "## Association Rule Mining\n",
        "Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs.\n",
        "\n",
        "### Rule Evaluation Metrics –\n",
        "\n",
        "#### Support(s) –\n",
        "The number of transactions that include items in the {X} and {Y} parts of the rule as a percentage of the total number of transaction.It is a measure of how frequently the collection of items occur together as a percentage of all transactions.\n",
        "\n",
        "It is interpreted as fraction of transactions that contain both X and Y.\n",
        "\n",
        "Support of the item x is nothing but the ratio of the number of transactions in which the item x appears to the total number of transactions.\n",
        "\n",
        "#### Confidence(c) –\n",
        "It is the ratio of the no of transactions that includes all items in {B} as well as the no of transactions that includes all items in {A} to the no of transactions that includes all items in {A}.\n",
        "Conf(X=>Y) = Supp(X\\cupY) \\div Supp(X) –\n",
        "It measures how often each item in Y appears in transactions that contains items in X also.\n",
        "\n",
        "Confidence (x => y) signifies the likelihood of the item y being purchased when the item x is purchased. This method takes into account the popularity of the item x.\n",
        "\n",
        "#### Lift(l) –\n",
        "The lift of the rule X=>Y is the confidence of the rule divided by the expected confidence, assuming that the itemsets X and Y are independent of each other.The expected confidence is the confidence divided by the frequency of {Y}.\n",
        "\n",
        "Lift (x => y) is nothing but the ‘interestingness’ or the likelihood of the item y being purchased when the item x is sold. Unlike confidence (x => y), this method takes into account the popularity of the item y.\n",
        "\n",
        "Lift value near 1 indicates X and Y almost often appear together as expected, greater than 1 means they appear together more than expected and less than 1 means they appear less than expected.Greater lift values indicate stronger association.\n",
        "\n",
        "#### Conviction -\n",
        "Conviction of a rule can be defined as follows:\n",
        "\n",
        "Its value range is [0, +∞].\n",
        "\n",
        "Conv(x => y) = 1 means that x has no relation with y.\n",
        "Greater the conviction higher the interest in the rule."
      ]
    },
    {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
                ]
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "d045d394-5c0c-40c7-f56f-cdc85a723468"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f9907fbf-d8fe-41bd-8692-bda728f2b74a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f9907fbf-d8fe-41bd-8692-bda728f2b74a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving new_telco (2).csv to new_telco (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-5IoADo5GBD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "fac89b30-757b-4df6-f69a-98b5a5e0ed7c"
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "telco = pd.read_csv(io.BytesIO(uploaded['new_telco (2).csv']))\n",
        "telco=telco.drop(['customerID','TotalCharges','Unnamed: 0'] , axis=1)\n",
        "telco.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>PartnerYes</td>\n",
              "      <td>Independent</td>\n",
              "      <td>1</td>\n",
              "      <td>PSNo</td>\n",
              "      <td>MLNo</td>\n",
              "      <td>DSL</td>\n",
              "      <td>OSNo</td>\n",
              "      <td>OBYes</td>\n",
              "      <td>DPNo</td>\n",
              "      <td>TSNo</td>\n",
              "      <td>STVNo</td>\n",
              "      <td>SMNo</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>PLBYes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>PartnerNo</td>\n",
              "      <td>Independent</td>\n",
              "      <td>34</td>\n",
              "      <td>PSYes</td>\n",
              "      <td>MLNo</td>\n",
              "      <td>DSL</td>\n",
              "      <td>OSYes</td>\n",
              "      <td>OBNo</td>\n",
              "      <td>DPYes</td>\n",
              "      <td>TSNo</td>\n",
              "      <td>STVNo</td>\n",
              "      <td>SMNo</td>\n",
              "      <td>One year</td>\n",
              "      <td>PLBNo</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>PartnerNo</td>\n",
              "      <td>Independent</td>\n",
              "      <td>2</td>\n",
              "      <td>PSYes</td>\n",
              "      <td>MLNo</td>\n",
              "      <td>DSL</td>\n",
              "      <td>OSYes</td>\n",
              "      <td>OBYes</td>\n",
              "      <td>DPNo</td>\n",
              "      <td>TSNo</td>\n",
              "      <td>STVNo</td>\n",
              "      <td>SMNo</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>PLBYes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>PartnerNo</td>\n",
              "      <td>Independent</td>\n",
              "      <td>45</td>\n",
              "      <td>PSNo</td>\n",
              "      <td>MLNo</td>\n",
              "      <td>DSL</td>\n",
              "      <td>OSYes</td>\n",
              "      <td>OBNo</td>\n",
              "      <td>DPYes</td>\n",
              "      <td>TSYes</td>\n",
              "      <td>STVNo</td>\n",
              "      <td>SMNo</td>\n",
              "      <td>One year</td>\n",
              "      <td>PLBNo</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>PartnerNo</td>\n",
              "      <td>Independent</td>\n",
              "      <td>2</td>\n",
              "      <td>PSYes</td>\n",
              "      <td>MLNo</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>OSNo</td>\n",
              "      <td>OBNo</td>\n",
              "      <td>DPNo</td>\n",
              "      <td>TSNo</td>\n",
              "      <td>STVNo</td>\n",
              "      <td>SMNo</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>PLBYes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen  ... MonthlyCharges Churn\n",
              "0  Female              0  ...          29.85    No\n",
              "1    Male              0  ...          56.95    No\n",
              "2    Male              0  ...          53.85   Yes\n",
              "3    Male              0  ...          42.30    No\n",
              "4  Female              0  ...          70.70   Yes\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meBNGapT5lAJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "25aa1e2c-0ca2-441c-e810-ce1884e213b4"
      },
      "source": [
        "telco['gender'].replace(to_replace=[['Female'],['Male']],value=[0,1],inplace=True)\n",
        "telco['Partner'].replace(to_replace=[['PartnerNo'],['PartnerYes']],value=[0,1],inplace=True)\n",
        "telco['Dependents'].replace(to_replace=[['Independent'],['Dependent']],value=[0,1],inplace=True)\n",
        "telco['PhoneService'].replace(to_replace=[['PSNo'],['PSYes']],value=[0,1],inplace=True)\n",
        "telco['PaperlessBilling'].replace(to_replace=[['PLBNo'],['PLBYes']],value=[0,1],inplace=True)\n",
        "telco['MultipleLines'].replace(to_replace=[['MLNo'],['MLYes']],value=[0,1],inplace=True)\n",
        "telco['OnlineSecurity'].replace(to_replace=[['OSNo'],['OSYes']],value=[0,1],inplace=True)\n",
        "telco['OnlineBackup'].replace(to_replace=[['OBNo'],['OBYes']],value=[0,1],inplace=True)\n",
        "telco['DeviceProtection'].replace(to_replace=[['DPNo'],['DPYes']],value=[0,1],inplace=True)\n",
        "telco['TechSupport'].replace(to_replace=[['TSNo'],['TSYes']],value=[0,1],inplace=True)\n",
        "telco['StreamingTV'].replace(to_replace=[['STVNo'],['STVYes']],value=[0,1],inplace=True)\n",
        "telco['StreamingMovies'].replace(to_replace=[['SMNo'],['SMYes']],value=[0,1],inplace=True)\n",
        "telco['InternetService'].replace(to_replace=[['DSL'],['Fiber optic'],['No']],value=[0,1,2],inplace=True)\n",
        "telco['PaymentMethod'].replace(to_replace=[['Electronic check'],['Mailed check'],['Bank transfer (automatic)'],['Credit card (automatic)']],value=[0,1,2,3],inplace=True)\n",
        "telco['Contract'].replace(to_replace=[['Month-to-month'], ['One year'], ['Two year']],value=[0,1,2],inplace=True)\n",
        "telco['Churn'].replace(to_replace=[['No'], ['Yes']],value=[0,1],inplace=True)\n",
        "telco.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>29.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>56.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>53.85</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>42.30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>70.70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen  Partner  ...  PaymentMethod  MonthlyCharges  Churn\n",
              "0       0              0        1  ...              0           29.85      0\n",
              "1       1              0        0  ...              1           56.95      0\n",
              "2       1              0        0  ...              1           53.85      1\n",
              "3       1              0        0  ...              2           42.30      0\n",
              "4       0              0        0  ...              0           70.70      1\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQ1KgIIv6DBz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=telco\n",
        "df_1=df[df['Churn']==1]\n",
        "df_0=df[df['Churn']==0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jaaaCD_6YqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "from mlxtend.frequent_patterns import apriori, association_rules "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZk7TrOt6ayC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8833dfb2-b278-400b-f184-6e8355b19675"
      },
      "source": [
        "# Different outcomes \n",
        "df.Churn.unique() "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvgrpRck7Q2k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def hot_encode(x): \n",
        "    if(x<= 0): \n",
        "        return 0\n",
        "    if(x>= 1): \n",
        "        return 1\n",
        "  \n",
        "# Encoding the datasets \n",
        "df_encoded = df.applymap(hot_encode) \n",
        "\n",
        "\n",
        "df_0_encoded= df_0.applymap(hot_encode) \n",
        "\n",
        "df_1_encoded = df_1.applymap(hot_encode) \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQAXL60v68zo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "1f7139db-aac3-4f91-e209-e9525e510f0a"
      },
      "source": [
        "# Building the model when Churn=1\n",
        "frq_items = apriori(df_1_encoded, min_support = 0.05, use_colnames = True) \n",
        "\n",
        "# Collecting the inferred rules in a dataframe \n",
        "rules = association_rules(frq_items, metric =\"lift\", min_threshold = 1) \n",
        "rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) \n",
        "rules.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26030</th>\n",
              "      <td>(MultipleLines, StreamingMovies, gender, Senio...</td>\n",
              "      <td>(InternetService)</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>0.754414</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.325532</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96269</th>\n",
              "      <td>(tenure, gender, SeniorCitizen, MultipleLines,...</td>\n",
              "      <td>(InternetService)</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>0.754414</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.325532</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96284</th>\n",
              "      <td>(MultipleLines, StreamingMovies, gender, Senio...</td>\n",
              "      <td>(InternetService, tenure)</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>0.754414</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.325532</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97771</th>\n",
              "      <td>(gender, PhoneService, SeniorCitizen, Multiple...</td>\n",
              "      <td>(InternetService)</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>0.754414</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.325532</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97785</th>\n",
              "      <td>(MultipleLines, StreamingMovies, gender, Senio...</td>\n",
              "      <td>(InternetService, PhoneService)</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>0.754414</td>\n",
              "      <td>0.050829</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.325532</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             antecedents  ... conviction\n",
              "26030  (MultipleLines, StreamingMovies, gender, Senio...  ...        inf\n",
              "96269  (tenure, gender, SeniorCitizen, MultipleLines,...  ...        inf\n",
              "96284  (MultipleLines, StreamingMovies, gender, Senio...  ...        inf\n",
              "97771  (gender, PhoneService, SeniorCitizen, Multiple...  ...        inf\n",
              "97785  (MultipleLines, StreamingMovies, gender, Senio...  ...        inf\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oOihBob9xkH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "899bc1a6-b989-43e8-c08e-29f1e3e0ef48"
      },
      "source": [
        "# Building the model when Churn=1\n",
        "frq_items = apriori(df_0_encoded, min_support = 0.05, use_colnames = True) \n",
        "\n",
        "# Collecting the inferred rules in a dataframe \n",
        "rules = association_rules(frq_items, metric =\"lift\", min_threshold = 1) \n",
        "rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) \n",
        "rules.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7906</th>\n",
              "      <td>(MultipleLines, SeniorCitizen)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.074411</td>\n",
              "      <td>0.899304</td>\n",
              "      <td>0.074411</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.111971</td>\n",
              "      <td>0.007493</td>\n",
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              "    <tr>\n",
              "      <th>7920</th>\n",
              "      <td>(InternetService, SeniorCitizen)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.093738</td>\n",
              "      <td>0.899304</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.111971</td>\n",
              "      <td>0.009439</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17042</th>\n",
              "      <td>(MultipleLines, StreamingMovies)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.212215</td>\n",
              "      <td>0.899304</td>\n",
              "      <td>0.212215</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.111971</td>\n",
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              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17100</th>\n",
              "      <td>(InternetService, OnlineSecurity)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.126788</td>\n",
              "      <td>0.899304</td>\n",
              "      <td>0.126788</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.111971</td>\n",
              "      <td>0.012767</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17106</th>\n",
              "      <td>(InternetService, OnlineBackup)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.180905</td>\n",
              "      <td>0.899304</td>\n",
              "      <td>0.180905</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.111971</td>\n",
              "      <td>0.018216</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             antecedents  ... conviction\n",
              "7906      (MultipleLines, SeniorCitizen)  ...        inf\n",
              "7920    (InternetService, SeniorCitizen)  ...        inf\n",
              "17042   (MultipleLines, StreamingMovies)  ...        inf\n",
              "17100  (InternetService, OnlineSecurity)  ...        inf\n",
              "17106    (InternetService, OnlineBackup)  ...        inf\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUE76aBO-b3I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "bfbc62db-c566-403c-ca28-5dea8c1a2309"
      },
      "source": [
        "# Building the model for the original dataset\n",
        "frq_items = apriori(df_encoded, min_support = 0.05, use_colnames = True) \n",
        "\n",
        "# Collecting the rules in a dataframe \n",
        "rules = association_rules(frq_items, metric =\"lift\", min_threshold = 1) \n",
        "rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) \n",
        "rules.head()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8072</th>\n",
              "      <td>(MultipleLines, SeniorCitizen)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.094420</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.094420</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.108785</td>\n",
              "      <td>0.009264</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8086</th>\n",
              "      <td>(InternetService, SeniorCitizen)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.125373</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.125373</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.108785</td>\n",
              "      <td>0.012301</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16864</th>\n",
              "      <td>(MultipleLines, StreamingMovies)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.225898</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.225898</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.108785</td>\n",
              "      <td>0.022163</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16924</th>\n",
              "      <td>(MultipleLines, Churn)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.120687</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.120687</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.108785</td>\n",
              "      <td>0.011841</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16936</th>\n",
              "      <td>(InternetService, OnlineSecurity)</td>\n",
              "      <td>(tenure, PhoneService)</td>\n",
              "      <td>0.119125</td>\n",
              "      <td>0.901888</td>\n",
              "      <td>0.119125</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.108785</td>\n",
              "      <td>0.011688</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             antecedents  ... conviction\n",
              "8072      (MultipleLines, SeniorCitizen)  ...        inf\n",
              "8086    (InternetService, SeniorCitizen)  ...        inf\n",
              "16864   (MultipleLines, StreamingMovies)  ...        inf\n",
              "16924             (MultipleLines, Churn)  ...        inf\n",
              "16936  (InternetService, OnlineSecurity)  ...        inf\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    }
  ]
}