Customer_Churn_Prediction_Using_Ensemble_Techniques.ipynb

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      "source": [
        "# **Customer Prediction for consulting purpose**"
      ],
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        "#load the data set\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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      "source": [
        "#load dataset\n",
        "df = pd.read_csv('Telco_customer_churn.csv')\n",
        "df.head()"
      ],
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      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "   CustomerID  Count        Country       State         City  Zip Code  \\\n",
              "0  3668-QPYBK      1  United States  California  Los Angeles     90003   \n",
              "1  9237-HQITU      1  United States  California  Los Angeles     90005   \n",
              "2  9305-CDSKC      1  United States  California  Los Angeles     90006   \n",
              "3  7892-POOKP      1  United States  California  Los Angeles     90010   \n",
              "4  0280-XJGEX      1  United States  California  Los Angeles     90015   \n",
              "\n",
              "                 Lat Long   Latitude   Longitude  Gender  ...        Contract  \\\n",
              "0  33.964131, -118.272783  33.964131 -118.272783    Male  ...  Month-to-month   \n",
              "1   34.059281, -118.30742  34.059281 -118.307420  Female  ...  Month-to-month   \n",
              "2  34.048013, -118.293953  34.048013 -118.293953  Female  ...  Month-to-month   \n",
              "3  34.062125, -118.315709  34.062125 -118.315709  Female  ...  Month-to-month   \n",
              "4  34.039224, -118.266293  34.039224 -118.266293    Male  ...  Month-to-month   \n",
              "\n",
              "  Paperless Billing             Payment Method  Monthly Charges Total Charges  \\\n",
              "0               Yes               Mailed check            53.85        108.15   \n",
              "1               Yes           Electronic check            70.70        151.65   \n",
              "2               Yes           Electronic check            99.65         820.5   \n",
              "3               Yes           Electronic check           104.80       3046.05   \n",
              "4               Yes  Bank transfer (automatic)           103.70        5036.3   \n",
              "\n",
              "  Churn Label Churn Value Churn Score  CLTV                   Churn Reason  \n",
              "0         Yes           1          86  3239   Competitor made better offer  \n",
              "1         Yes           1          67  2701                          Moved  \n",
              "2         Yes           1          86  5372                          Moved  \n",
              "3         Yes           1          84  5003                          Moved  \n",
              "4         Yes           1          89  5340  Competitor had better devices  \n",
              "\n",
              "[5 rows x 33 columns]"
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              "      <th>CustomerID</th>\n",
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              "      <td>3668-QPYBK</td>\n",
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              "      <td>9237-HQITU</td>\n",
              "      <td>1</td>\n",
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              "      <td>34.059281, -118.30742</td>\n",
              "      <td>34.059281</td>\n",
              "      <td>-118.307420</td>\n",
              "      <td>Female</td>\n",
              "      <td>...</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
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              "      <td>34.048013</td>\n",
              "      <td>-118.293953</td>\n",
              "      <td>Female</td>\n",
              "      <td>...</td>\n",
              "      <td>Month-to-month</td>\n",
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              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>86</td>\n",
              "      <td>5372</td>\n",
              "      <td>Moved</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7892-POOKP</td>\n",
              "      <td>1</td>\n",
              "      <td>United States</td>\n",
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              "      <td>Los Angeles</td>\n",
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              "      <td>1</td>\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-030098b5-65c5-417f-873f-e7114e9db8f7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-030098b5-65c5-417f-873f-e7114e9db8f7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-030098b5-65c5-417f-873f-e7114e9db8f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preprocessing\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vfBjsdEUrj5r",
        "outputId": "d2ba8a59-e6ed-4df1-eec0-982463f1038f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID            object\n",
              "Count                  int64\n",
              "Country               object\n",
              "State                 object\n",
              "City                  object\n",
              "Zip Code               int64\n",
              "Lat Long              object\n",
              "Latitude             float64\n",
              "Longitude            float64\n",
              "Gender                object\n",
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              "Dependents            object\n",
              "Tenure Months          int64\n",
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              "Online Backup         object\n",
              "Device Protection     object\n",
              "Tech Support          object\n",
              "Streaming TV          object\n",
              "Streaming Movies      object\n",
              "Contract              object\n",
              "Paperless Billing     object\n",
              "Payment Method        object\n",
              "Monthly Charges      float64\n",
              "Total Charges         object\n",
              "Churn Label           object\n",
              "Churn Value            int64\n",
              "Churn Score            int64\n",
              "CLTV                   int64\n",
              "Churn Reason          object\n",
              "dtype: object"
            ],
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              "      <th>Paperless Billing</th>\n",
              "      <td>object</td>\n",
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              "      <th>Payment Method</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monthly Charges</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Charges</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Label</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Value</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Score</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CLTV</th>\n",
              "      <td>int64</td>\n",
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              "    <tr>\n",
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              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cacTaAIpswPO",
        "outputId": "d6a042d6-0927-4ad4-ea46-89986c3392be"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID              0\n",
              "Count                   0\n",
              "Country                 0\n",
              "State                   0\n",
              "City                    0\n",
              "Zip Code                0\n",
              "Lat Long                0\n",
              "Latitude                0\n",
              "Longitude               0\n",
              "Gender                  0\n",
              "Senior Citizen          0\n",
              "Partner                 0\n",
              "Dependents              0\n",
              "Tenure Months           0\n",
              "Phone Service           0\n",
              "Multiple Lines          0\n",
              "Internet Service        0\n",
              "Online Security         0\n",
              "Online Backup           0\n",
              "Device Protection       0\n",
              "Tech Support            0\n",
              "Streaming TV            0\n",
              "Streaming Movies        0\n",
              "Contract                0\n",
              "Paperless Billing       0\n",
              "Payment Method          0\n",
              "Monthly Charges         0\n",
              "Total Charges           0\n",
              "Churn Label             0\n",
              "Churn Value             0\n",
              "Churn Score             0\n",
              "CLTV                    0\n",
              "Churn Reason         5174\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>Paperless Billing</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment Method</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monthly Charges</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Charges</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Label</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Value</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Score</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>CLTV</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn Reason</th>\n",
              "      <td>5174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data preprocessing\n",
        "df.drop('CustomerID',axis=1,inplace=True)\n",
        "df['Total Charges'] = pd.to_numeric(df['Total Charges'],errors='coerce')\n",
        "df.dropna(inplace=True)\n",
        "df=pd.get_dummies(df,drop_first=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "jwQ8jYdnsM2s",
        "outputId": "2703d2f5-d717-452a-dc3e-20ba9c8105ff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Count  Zip Code   Latitude   Longitude  Tenure Months  Monthly Charges  \\\n",
              "0      1     90003  33.964131 -118.272783              2            53.85   \n",
              "1      1     90005  34.059281 -118.307420              2            70.70   \n",
              "2      1     90006  34.048013 -118.293953              8            99.65   \n",
              "3      1     90010  34.062125 -118.315709             28           104.80   \n",
              "4      1     90015  34.039224 -118.266293             49           103.70   \n",
              "\n",
              "   Total Charges  Churn Value  Churn Score  CLTV  ...  \\\n",
              "0         108.15            1           86  3239  ...   \n",
              "1         151.65            1           67  2701  ...   \n",
              "2         820.50            1           86  5372  ...   \n",
              "3        3046.05            1           84  5003  ...   \n",
              "4        5036.30            1           89  5340  ...   \n",
              "\n",
              "   Churn Reason_Lack of self-service on Website  \\\n",
              "0                                         False   \n",
              "1                                         False   \n",
              "2                                         False   \n",
              "3                                         False   \n",
              "4                                         False   \n",
              "\n",
              "   Churn Reason_Limited range of services  Churn Reason_Long distance charges  \\\n",
              "0                                   False                               False   \n",
              "1                                   False                               False   \n",
              "2                                   False                               False   \n",
              "3                                   False                               False   \n",
              "4                                   False                               False   \n",
              "\n",
              "   Churn Reason_Moved  Churn Reason_Network reliability  \\\n",
              "0               False                             False   \n",
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              "4                                          False   \n",
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              "   Churn Reason_Poor expertise of phone support  Churn Reason_Price too high  \\\n",
              "0                                         False                        False   \n",
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              "2                                 False                                 False  \n",
              "3                                 False                                 False  \n",
              "4                                 False                                 False  \n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Define features and target on churn\n",
        "X=df.drop('Churn Score',axis=1)\n",
        "y=df['Churn Score']\n"
      ],
      "metadata": {
        "id": "cdQ7cIQSwDik"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split the data\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
        "print(X_train.shape,X_test.shape)\n",
        "print(y_train.shape,y_test.shape)\n",
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xq-Ab2J9xadB",
        "outputId": "b4460c70-a328-4d4a-efa6-69e996e4a2aa"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1495, 2076) (374, 2076)\n",
            "(1495,) (374,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#random forest model\n",
        "rf = RandomForestClassifier(n_estimators=100,random_state=42)\n",
        "rf.fit(X_train,y_train)\n",
        "y_pred = rf.predict(X_test)\n",
        "\n",
        "print(accuracy_score(y_test,y_pred))\n",
        "# print(classification_report(y_test,y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHaHqFwsxyr8",
        "outputId": "74d65d19-4136-4dab-c0c4-59b183f52601"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.026737967914438502\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#adaBoost model\n",
        "ada = AdaBoostClassifier(n_estimators=100,random_state=42)\n",
        "ada.fit(X_train,y_train)\n",
        "y_pred = ada.predict(X_test)\n",
        "\n",
        "print(accuracy_score(y_test,y_pred))\n",
        "# print(classification_report(y_test,y_pred))\n",
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRiqtK7_zi4r",
        "outputId": "ed8e8409-6d6a-46d1-a506-6f078d550228"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.034759358288770054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print feature ranking\n",
        "feature_importance = pd.Series(rf.feature_importances_,index=X.columns).sort_values(ascending=False)\n",
        "print(feature_importance)\n",
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNVrTuH60MOZ",
        "outputId": "316081b7-292f-4184-cdde-4a5a0462f37e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Charges                      0.036366\n",
            "Monthly Charges                    0.035779\n",
            "CLTV                               0.035638\n",
            "Zip Code                           0.033826\n",
            "Longitude                          0.033196\n",
            "                                     ...   \n",
            "Lat Long_37.872902, -122.303708    0.000000\n",
            "City_Pine Grove                    0.000000\n",
            "City_Platina                       0.000000\n",
            "Lat Long_37.815459, -121.977203    0.000000\n",
            "Count                              0.000000\n",
            "Length: 2076, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the feature of important only limited\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.barplot(x=feature_importance.head(10),y=feature_importance.head(10).index)\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title('Top 10 Most Important Features')\n",
        "plt.show()\n",
        "#\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "syJ2q4-g0cEZ",
        "outputId": "cefa3c5c-054f-41df-927f-1de35fe36942"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#apply gradiantboostingclassifer\n",
        "gb = GradientBoostingClassifier(n_estimators=100,random_state=42)\n",
        "gb.fit(X_train,y_train)\n",
        "y_pred = gb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred))\n",
        "\n",
        "# print(classification_report(y_test,y_pred))\n",
        "# print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rI2y76DQ2Hz5",
        "outputId": "ed8c9b39-dc51-4398-a840-6b5f5c4a204b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.029411764705882353\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bagging and Booting\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "#use on above training and testing dataset\n",
        "bag_clf = BaggingClassifier(\n",
        "    DecisionTreeClassifier(), n_estimators=500,\n",
        "    max_samples=100, bootstrap=True, n_jobs=-1)\n",
        "bag_clf.fit(X_train, y_train)\n",
        "y_pred = bag_clf.predict(X_test)\n",
        "\n",
        "#print the prediction\n",
        "print(accuracy_score(y_test,y_pred))\n",
        "# print(classification_report(y_test,y_pred))\n",
        "# print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zOz58rp3TxV",
        "outputId": "04f55c19-41d0-49a8-e086-dbdb4641f1c9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.01871657754010695\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#use the Voting Classifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "#use on above training and testing dataset\n",
        "log_clf = LogisticRegression()\n",
        "rnd_clf = RandomForestClassifier()\n",
        "svm_clf = SVC()\n",
        "\n",
        "voting_clf = VotingClassifier(\n",
        "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
        "    voting='hard')\n",
        "voting_clf.fit(X_train, y_train)\n",
        "y_pred = voting_clf.predict(X_test)\n",
        "\n",
        "#print the prediction\n",
        "print(accuracy_score(y_test,y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bgWJ0FF4v7q",
        "outputId": "889a6517-cf32-4ad8-83fe-c84c34652a9d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.026737967914438502\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Ensemble Techniques in One Shot**"
      ],
      "metadata": {
        "id": "lRRJ_SCe-TpF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1: Import Libraries and Load Data**"
      ],
      "metadata": {
        "id": "2TIy6vGY-pV0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "metadata": {
        "id": "1GgknLvp-kx8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "qpwk0NKd-8vV",
        "outputId": "bbbbf533-fd11-47fc-eb71-42301fe266c2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-07bd6311-b74e-4453-9a3d-18d087d4b582\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-07bd6311-b74e-4453-9a3d-18d087d4b582\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Telco-Customer-Churns.csv to Telco-Customer-Churns.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv('Telco-Customer-Churns.csv')\n",
        "\n",
        "# Display the first few rows\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "CN9Pq3r1-lbc",
        "outputId": "658f867d-0864-48f0-fb28-7c06a884adf7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "\n",
              "  <div id=\"df-240605a9-f077-45f4-8e90-db9c6832386a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
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              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
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              "      <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>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-buttons div {\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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              "\n",
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              "        document.querySelector('#df-240605a9-f077-45f4-8e90-db9c6832386a button.colab-df-convert');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-11dc1490-7623-4cfc-ac2e-a057891a4a2e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11dc1490-7623-4cfc-ac2e-a057891a4a2e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11dc1490-7623-4cfc-ac2e-a057891a4a2e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2: Data Preprocessing**\n",
        "\n",
        "Since churn datasets often have categorical columns, encode these as numbers."
      ],
      "metadata": {
        "id": "khfgOQFW_V4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical columns to numerical values\n",
        "le = LabelEncoder()\n",
        "for column in data.select_dtypes(include=['object']).columns:\n",
        "    data[column] = le.fit_transform(data[column])\n",
        "\n",
        "# Define features (X) and target variable (y)\n",
        "X = data.drop('Churn', axis=1)  # Adjust if 'Churn' is named differently\n",
        "y = data['Churn']\n",
        "\n",
        "# Split data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "9XspUcYo_Zoc"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3: Ensemble Models**\n",
        "\n",
        "1. Random Forest"
      ],
      "metadata": {
        "id": "F_ctafTLAMCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf.fit(X_train, y_train)\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "print(\"Random Forest Accuracy:\", accuracy_score(y_test, y_pred_rf))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LX_BoABeAN2s",
        "outputId": "f202bfbb-83db-4f07-fb3a-7265c5f0ab82"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.7970191625266146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Bagging Classifier**"
      ],
      "metadata": {
        "id": "ykMR9X1QAQoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Bagging Classifier with updated 'estimator' parameter\n",
        "bagging = BaggingClassifier(estimator=SVC(), n_estimators=10, random_state=42)\n",
        "bagging.fit(X_train, y_train)\n",
        "y_pred_bagging = bagging.predict(X_test)\n",
        "print(\"Bagging Classifier Accuracy:\", accuracy_score(y_test, y_pred_bagging))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4_Ogd0nAUuL",
        "outputId": "8d992e59-a49e-4e16-9e79-a17b16f82c41"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bagging Classifier Accuracy: 0.7352732434350603\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. AdaBoost**"
      ],
      "metadata": {
        "id": "Jq5JW2rcA_wm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ada = AdaBoostClassifier(n_estimators=50, random_state=42)\n",
        "ada.fit(X_train, y_train)\n",
        "y_pred_ada = ada.predict(X_test)\n",
        "print(\"AdaBoost Accuracy:\", accuracy_score(y_test, y_pred_ada))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_iDUtQyBFB6",
        "outputId": "a50099b9-f7b6-4427-ad50-6f6f61163f31"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoost Accuracy: 0.8062455642299503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Gradient Boosting**"
      ],
      "metadata": {
        "id": "F5C1Ku76AYwW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gb = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
        "gb.fit(X_train, y_train)\n",
        "y_pred_gb = gb.predict(X_test)\n",
        "print(\"Gradient Boosting Accuracy:\", accuracy_score(y_test, y_pred_gb))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHVn-WQmBLlj",
        "outputId": "d5e8818f-f272-4a23-9b9a-3e330b3276e1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Accuracy: 0.808374733853797\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Voting Classifier**\n",
        "\n",
        "Combine all classifiers in a voting ensemble."
      ],
      "metadata": {
        "id": "dEh3buEhBOMa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "voting = VotingClassifier(estimators=[\n",
        "    ('rf', rf), ('bagging', bagging), ('ada', ada), ('gb', gb)], voting='hard')\n",
        "voting.fit(X_train, y_train)\n",
        "y_pred_voting = voting.predict(X_test)\n",
        "print(\"Voting Classifier Accuracy:\", accuracy_score(y_test, y_pred_voting))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z55Q5I_nBSNM",
        "outputId": "566de538-bc46-4024-8855-084b434bbaa1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Voting Classifier Accuracy: 0.7927608232789212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization**\n",
        "\n",
        "**Step 1: Churn Distribution Plot**\n",
        "\n",
        "Churn- Manthan"
      ],
      "metadata": {
        "id": "7bh0_sqWBUQj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Plot the churn distribution\n",
        "sns.countplot(x='Churn', data=data)\n",
        "plt.title('Churn Distribution')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Emahs03NBcok",
        "outputId": "a4c921fe-979a-4299-a592-802aef48231f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2: Pair Plot**\n",
        "\n",
        "A pair plot provides a grid of scatter plots for each pair of numerical features, colored by the churn outcome."
      ],
      "metadata": {
        "id": "B51w1b39CRks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pair plot to visualize relationships between features and Churn\n",
        "sns.pairplot(data, hue='Churn', plot_kws={'alpha': 0.5})\n",
        "plt.suptitle(\"Pair Plot of Features by Churn\", y=1.02)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "2fZIk_AGDGMT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3: Correlation Heatmap**\n",
        "\n",
        "The heatmap helps identify correlations between features and with the target variable."
      ],
      "metadata": {
        "id": "sPPuVFfwCYea"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap\n",
        "plt.figure(figsize=(12, 8))\n",
        "correlation_matrix = data.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lEsZNuNyDN-k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 4: Box Plot of Features by Churn**\n",
        "\n",
        "Box plots show the distribution of features with respect to churn, which is useful to detect if a particular feature has different distributions for churned vs. non-churned customers."
      ],
      "metadata": {
        "id": "FO01jpGWCgBQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select numerical features only\n",
        "numerical_features = data.select_dtypes(include=['float64', 'int64']).columns\n",
        "\n",
        "# Box plot of numerical features by Churn\n",
        "plt.figure(figsize=(15, 10))\n",
        "for i, feature in enumerate(numerical_features, 1):\n",
        "    plt.subplot(3, 3, i)\n",
        "    sns.boxplot(x='Churn', y=feature, data=data)\n",
        "    plt.title(f'Box Plot of {feature} by Churn')\n",
        "    plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "h7gmM9FMCj6L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}