Churn_modelling.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1azVSrE7heO"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action = 'ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.Load the dataset."
      ],
      "metadata": {
        "id": "7N3rg11deVUg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Churn_Modelling.csv')"
      ],
      "metadata": {
        "id": "2lPRmOV58KOV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
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        },
        "id": "d_8ktunV8qL-",
        "outputId": "7f9b1547-6515-48cc-9375-3f8fd4d72e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber            int64\n",
              "CustomerId           int64\n",
              "Surname             object\n",
              "CreditScore          int64\n",
              "Geography           object\n",
              "Gender              object\n",
              "Age                  int64\n",
              "Tenure               int64\n",
              "Balance            float64\n",
              "NumOfProducts        int64\n",
              "HasCrCard            int64\n",
              "IsActiveMember       int64\n",
              "EstimatedSalary    float64\n",
              "Exited               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.select_dtypes(include=['int64','float64','Int64']).dtypes"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "FZzORaeQ8uxU",
        "outputId": "79626bc8-cf1f-4835-fcd0-ba832ab4d7ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber            int64\n",
              "CustomerId           int64\n",
              "CreditScore          int64\n",
              "Age                  int64\n",
              "Tenure               int64\n",
              "Balance            float64\n",
              "NumOfProducts        int64\n",
              "HasCrCard            int64\n",
              "IsActiveMember       int64\n",
              "EstimatedSalary    float64\n",
              "Exited               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['Surname']).agg({'RowNumber':'count', 'Exited':'mean'}\n",
        "                                  ).reset_index().sort_values(by='RowNumber', ascending=False).head()"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Surname  RowNumber    Exited\n",
              "2473   Smith         32  0.281250\n",
              "1689  Martin         29  0.310345\n",
              "2389   Scott         29  0.103448\n",
              "2751  Walker         28  0.142857\n",
              "336    Brown         26  0.192308"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Surname</th>\n",
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              "      <th>Exited</th>\n",
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              "      <th>2473</th>\n",
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              "      <td>0.281250</td>\n",
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              "      <td>Martin</td>\n",
              "      <td>29</td>\n",
              "      <td>0.310345</td>\n",
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              "    <tr>\n",
              "      <th>2389</th>\n",
              "      <td>Scott</td>\n",
              "      <td>29</td>\n",
              "      <td>0.103448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2751</th>\n",
              "      <td>Walker</td>\n",
              "      <td>28</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>Brown</td>\n",
              "      <td>26</td>\n",
              "      <td>0.192308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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      "cell_type": "markdown",
      "source": [
        " 3.Perform Below Visualizations."
      ],
      "metadata": {
        "id": "F3B8JelIfBuJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#univariate analysis\n",
        "sns.set(style=\"whitegrid\")\n",
        "sns.boxplot(y=data['CreditScore'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
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        "outputId": "93bdcaee-044f-4c08-fdfc-b5c700494279"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f291b710410>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bivariate analysis\n",
        "credit_score = df['CreditScore']\n",
        "balance = df['Balance']\n",
        "credit_score.dropna()\n",
        "balance.dropna()\n",
        "print(credit_score[2])\n",
        "print(balance[2])\n",
        "plt.scatter(credit_score , balance)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "jxCYMTX76MVO",
        "outputId": "a38284c3-d828-4297-e435-50c41b3ebec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "502\n",
            "159660.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#multivariate\n",
        "result = pd.pivot_table(data=data, index='Geography', columns='Tenure',values='Age')\n",
        "sns.heatmap(result, annot=True, cmap = 'RdYlGn_r').set_title('Multivariate analysis')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "t1D7Z7mF75h1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "305d0ced-cf20-4d06-f4a4-b89755967a79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GE3f8ioZbrIr",
        "outputId": "14830cc5-18cd-4227-f58d-aef9e9f772ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.Perform descriptive statistics on the dataset."
      ],
      "metadata": {
        "id": "mewf4wjOfoNr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Age'].mode()"
      ],
      "metadata": {
        "id": "IwRYBhpNb8Nw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "072eccf6-843a-4fe8-c517-0e35ad83b68c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    37\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Age\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbZkglGNcpJy",
        "outputId": "20c0dd77-4f93-40bf-a323-075a2175f7ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.9218"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=round(data[\"Age\"].mean())"
      ],
      "metadata": {
        "id": "rdOeGdOTc5sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Age\"].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcxgjXXKc8VA",
        "outputId": "13969a6f-aed4-4d0a-e4b2-3b540577587d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.0"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s=round(data['Age'].std())\n",
        "print(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdC6kTc-dajA",
        "outputId": "3988ef5b-8cfb-4052-9d92-fee20c7f3131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.Handle missing values"
      ],
      "metadata": {
        "id": "ytyEwgIM3iRV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Iiw0cN8J3eFl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check missing values\n",
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtoqrBif3eTW",
        "outputId": "034b9acb-5563-4461-f895-7d690bd9cb1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          0\n",
              "CustomerId         0\n",
              "Surname            0\n",
              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
              "Balance            0\n",
              "NumOfProducts      0\n",
              "HasCrCard          0\n",
              "IsActiveMember     0\n",
              "EstimatedSalary    0\n",
              "Exited             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6.Find the outliers and replace the outliers"
      ],
      "metadata": {
        "id": "7YZ8XVyu5zGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CreditScores = data['CreditScore']\n",
        "CreditScores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7BNNvdM3nM2",
        "outputId": "dbd6e440-e1b0-4db5-8dc0-48f7a5fba843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       619\n",
              "1       608\n",
              "2       502\n",
              "3       699\n",
              "4       850\n",
              "       ... \n",
              "9995    771\n",
              "9996    516\n",
              "9997    709\n",
              "9998    772\n",
              "9999    792\n",
              "Name: CreditScore, Length: 10000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.boxplot(data['CreditScore'],showmeans = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HQtGPjLl3utN",
        "outputId": "f6a6fa64-2b91-42d4-936f-e15f8bcdc64f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD7CAYAAACL+TRnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAP20lEQVR4nO3dX2iUV4PH8d8zk2SswZBMSuIYoaIvldSBtkyWXolg2upCqmW9iA3oRVtK/9AqrdJgaCJSwcRc1CpC2+1NwSp0u2pNfY3s2l2wvLRmaAqTSCNRwWJM6MTUNJpJnDl7IRleq5l5xu38ief7AdHMeSbPGTDfOTmZyeMYY4wAAA81T74nAADIPmIPABYg9gBgAWIPABYg9gBggaJ8T+B+EomEJiYmVFxcLMdx8j0dAJgTjDGanp5WaWmpPJ671/IFGfuJiQkNDAzkexoAMCc9/vjjWrBgwV23FWTsi4uLJd2ZcElJSZ5nA9wtEokoGAzmexrAPaampjQwMJBs6D8ryNjPbN2UlJTI5/PleTbAvfh/iUJ2v+1vfkALABYg9gBgAWIPABYg9gBgAWIPABYg9gBgAWIPABYg9rBeMBiU4ziu/9TV1WV0vOM4vAkLeVeQb6oCcikSiWR0vOM44gJvmGtY2QOABYg9AFiA2AOABYg9AFiA2AOABYg9AFiA2AOABYg9AFiA2AOABYg9AFiA2AOABYg9AFiA2AOABYg9AFjAVey/++47vfjii1q/fr3WrVun06dPS5IuXbqkxsZGrVmzRo2Njbp8+XLyPqnGAAA5ZtJIJBKmrq7O/PLLL8YYY86fP2+eeuopE4/HzaZNm8yxY8eMMcYcO3bMbNq0KXm/VGPpTE5Omp6eHjM5Oen6PkCuuPiyAfIiVTtdrew9Ho/Gx8clSePj46qqqtL169fV39+vhoYGSVJDQ4P6+/s1OjqqaDQ66xgAIPfSXqnKcRx99NFHevPNNzV//nxNTEzo008/1dDQkKqrq+X1eiVJXq9XVVVVGhoakjFm1jG/35/dRwQAuEfa2N++fVuffPKJDh48qFAopHA4rK1bt6qjoyPrk8v0cnFAroTD4XxPAchI2tifP39eIyMjCoVCkqRQKKRHHnlEPp9Pw8PDisfj8nq9isfjGhkZUSAQkDFm1rFMBINB+Xy+B3tkQBbNfD0AhSQWi826SE67Z79w4UJdu3ZNFy9elCQNDg4qGo3qscceU21trbq6uiRJXV1dqq2tld/vV2Vl5axjAIDcc4wxJt1B33zzjT777DM5jiNJeuedd/Tss89qcHBQzc3NunHjhsrKytTe3q6lS5dKUsqxdGaenVjZoxA5jiMXXzZAzqVqp6vY5xqxRyEj9ihUqdrJO2gBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwALEHgAsQOwBwAJF+Z4A8Ffy+/26fv161s/jOE5WP39FRYVGR0ezeg7YhdjjoXL9+nUZY7J6jnA4rFAolNVzZPvJBPZhGwcALJB2Zf/rr7/qrbfeSn48Pj6uP/74Qz/++KMuXbqk5uZmjY2Nqby8XO3t7VqyZIkkpRwDAORW2pX94sWLdfz48eSf+vp6NTQ0SJLa2trU1NSk7u5uNTU1qbW1NXm/VGMAgNzKaBtnampKJ06c0IYNGxSNRtXf358Mf0NDg/r7+zU6OppyDACQexnF/syZM6qurtaKFSs0NDSk6upqeb1eSZLX61VVVZWGhoZSjgEAci+jV+N8/fXX2rBhQ7bmco9IJJKzc+HhEQ6HOQfwJ65jPzw8rHPnzqmjo0OSFAgENDw8rHg8Lq/Xq3g8rpGREQUCARljZh3LRDAYlM/ny+wRwXrZfllkLl56KWX/ceDhE4vFZl0ku97GOXr0qFatWqWKigpJUmVlpWpra9XV1SVJ6urqUm1trfx+f8oxAEDuuV7ZHz16VC0tLXfdtnPnTjU3N+vgwYMqKytTe3u7qzEAQG65jn13d/c9ty1btkxfffXVfY9PNQYAyC3eQQsAFiD2AGABYg8AFiD2AGABYg8AFiD2AGABYg8AFiD2AGABYg8AFiD2AGABYg8AFiD2AGABYg9k4Pqt3/Xlr10au/V7vqcCZITYAxn4uu+krkxe03/0ncz3VICMZHRZQqDQ/f3tel3cnZ1LZ97wenTmsUrJ4+jMhf/Vv/zXf2pBPJGVc/397fqsfF7Yi9jjofKv+/9bxpisfO5/7zksXfpeSsSloiKde/bf9GrdS1k51zLHkfk4K58almIbB3Dh+q3f9d3lf+h2Ii5Jup2I638u/4O9e8wZxB5w4eu+kzLm7i2bhEmwd485g9gDLgxELyZX9TNuJ+IaiF7M04yAzLBnD7jQsaYl+e9wOKxQKJTH2QCZY2UPABYg9gBgAWIPABYg9gBgAWIPABYg9gBgAWIPABYg9gBgAWIPABYg9gBgAWIPABZwFftYLKa2tjY9//zzeuGFF/TBBx9Iki5duqTGxkatWbNGjY2Nunz5cvI+qcYAALnlKvZ79+6Vz+dTd3e3Tpw4oS1btkiS2tra1NTUpO7ubjU1Nam1tTV5n1RjAIDcShv7iYkJHTt2TFu2bJHjOJKkRx99VNFoVP39/WpoaJAkNTQ0qL+/X6OjoynHAAC5l/ZXHF+5ckXl5eU6cOCAfvjhB5WWlmrLli2aN2+eqqur5fV6JUler1dVVVUaGhqSMWbWMb/fn91HBAC4R9rYx+NxXblyRU888YTef/99/fzzz3r99de1b9++rE8uEolk/Rx4+ITDYc4B/Ena2AcCARUVFSW3ZJ588klVVFRo3rx5Gh4eVjwel9frVTwe18jIiAKBgIwxs45lIhgMyufzPdgjg7WyfWGRXF28hAukIFOxWGzWRXLaPXu/369nnnlG33//vaQ7r7KJRqNasmSJamtr1dXVJUnq6upSbW2t/H6/KisrZx0DAOSeY4wx6Q66cuWKduzYobGxMRUVFWnr1q1atWqVBgcH1dzcrBs3bqisrEzt7e1aunSpJKUcS2fm2YmVPTLlOI5c/Jf+f8nFyj4XjwMPn1TtdBX7XCP2eFDEHjZL1U7eQQsAFkj7A1pgrpl5P8hcVlFRke8p4CFD7PFQycXWB1ssmIvYxgEACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxB7ALAAsQcACxS5OWj16tUqKSmRz+eTJG3btk0rV65Ub2+vWltbFYvFVFNTo71796qyslKSUo4BAHLL9cr+448/1vHjx3X8+HGtXLlSiURC27dvV2trq7q7u1VXV6fOzk5JSjkGAMi9B97GiUQi8vl8qqurkyRt3LhRp06dSjsGAMg9V9s40p2tG2OMQqGQ3n33XQ0NDWnRokXJcb/fr0QiobGxsZRj5eXlf+0jAACk5Sr2hw4dUiAQ0NTUlHbv3q1du3bpueeey/bcFIlEsn4O4EGEw+F8TwHIiKvYBwIBSVJJSYmampr0xhtvaPPmzbp69WrymNHRUXk8HpWXlysQCMw6lolgMJj8oTBQSEKhUL6nANwjFovNukhOu2d/8+ZNjY+PS5KMMTp58qRqa2sVDAY1OTmpnp4eSdKRI0e0du1aSUo5BgDIvbQr+2g0qrffflvxeFyJRELLli1TW1ubPB6POjo61NbWdtfLKyWlHAMA5J5jjDH5nsSfzXwrwjYOCpHjOCrALxsgZTt5By0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWCCj2B84cEDLly/XwMCAJKm3t1fr1q3TmjVr9PLLLysajSaPTTUGAMgt17Hv6+tTb2+vampqJEmJRELbt29Xa2ururu7VVdXp87OzrRjAIDccxX7qakp7dq1Szt37kzeFolE5PP5VFdXJ0nauHGjTp06lXYMAJB7rmK/b98+rVu3TosXL07eNjQ0pEWLFiU/9vv9SiQSGhsbSzkGAMi9onQH/PTTT4pEItq2bVsu5nOXSCSS83MCboTD4XxPAchI2tifO3dOg4ODqq+vlyRdu3ZNr7zyijZt2qSrV68mjxsdHZXH41F5ebkCgcCsY5kIBoPy+XwZ3QfIhVAolO8pAPeIxWKzLpLTbuO89tprOnv2rM6cOaMzZ85o4cKF+vzzz/Xqq69qcnJSPT09kqQjR45o7dq1ku5EerYxAEDupV3Zz8bj8aijo0NtbW2KxWKqqanR3r17044BAHLPMcaYfE/iz2a+FWEbB4Xk8OHD2r17t/r6+rRixQq1tLTopZdeyve0gKRU7eQdtIALhw8fVktLi/bv3y9J2r9/v1paWnT48OE8zwxwh5U9rBcMBtXX15fVc6xYsYJXlyHrUrXzgffsgYeFmwh7PHe+Ca6urtbIyIiqqqo0PDws6c47xoFCR+wBF4wxWrBggb788kvNnz9fN2/e1Pr16zU+Pp7vqQGusGcPuDR//vyUHwOFjJU94FJZWZnq6+tljJHjOPrb3/6W3MoBCh0re8AFn8+nCxcuqLS0VJJUWlqqCxcu8AICzBnEHnBhenpaknTr1q27/p65HSh0xB5wIZFI6Omnn06+8ubPHwOFjtgDLvX29qqzs1Nnz55VZ2enent78z0lwDViDwAW4NU4gEvGGL333nv5ngbwQFjZAxkoLi6+629griD2QAb27Nmjs2fPas+ePfmeCpARtnEAlzZv3qwdO3YoFovJ5/Np8+bN+uKLL/I9LcAVVvaAS+FwOHkFtsnJSa5DizmF2AMu+P1+9fX1KRgM6tq1a8lfi+z3+/M9NcAVtnEAF6LRqCorK9XX16eGhgZJd54AotFonmcGuMPKHnApGo3KGKOenh4ZYwg95hRiDwAWIPYAYAFiDwAWIPYAYIGCfDWOMUaSNDU1leeZAPcXi8XyPQXgHjPNnGnoPyvI2M9cEGJgYCDPMwHuLxKJ5HsKwKymp6c1b968u25zzP2eAvIskUhoYmJCxcXFchwn39MBgDnBGKPp6WmVlpbK47l7l74gYw8A+GvxA1oAsACxBwALEHsAsACxBwALEHsAsACxBwALEHsAsACxB1xqb2/X6tWrtXz5ct7djTmH2AMu1dfX69ChQ6qpqcn3VICMFeTvxgEKUV1dXb6nADwwVvYAYAFiDwAWIPYAYAFiDwAW4FccAy59+OGHOn36tH777TdVVFSovLxc3377bb6nBbhC7AHAAmzjAIAFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWIDYA4AFiD0AWOD/AHYwcCyBv7nkAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data[data['CreditScore'] >= 378]\n",
        "for i in data['CreditScore']:\n",
        "    if(i<378):\n",
        "        print(i)\n",
        "print(data['CreditScore'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcN_udFu4BGv",
        "outputId": "7e64407f-83a3-4671-c2f8-8681efe31e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "376\n",
            "376\n",
            "363\n",
            "359\n",
            "350\n",
            "350\n",
            "358\n",
            "351\n",
            "365\n",
            "367\n",
            "350\n",
            "350\n",
            "373\n",
            "350\n",
            "0       619\n",
            "1       608\n",
            "2       502\n",
            "3       699\n",
            "4       850\n",
            "       ... \n",
            "9995    771\n",
            "9996    516\n",
            "9997    709\n",
            "9998    772\n",
            "9999    792\n",
            "Name: CreditScore, Length: 10000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = data['Balance']\n",
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bvWXlAX4vDG",
        "outputId": "4fac241f-911b-4686-da40-46621fdd5de5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            0.00\n",
              "1        83807.86\n",
              "2       159660.80\n",
              "3            0.00\n",
              "4       125510.82\n",
              "          ...    \n",
              "9995         0.00\n",
              "9996     57369.61\n",
              "9997         0.00\n",
              "9998     75075.31\n",
              "9999    130142.79\n",
              "Name: Balance, Length: 10000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(b)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "oUmN7yHt3gQl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "665289e5-3994-405f-e3d0-2b2adf27a390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAD7CAYAAABE+8LhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPRElEQVR4nO3dX2hT9//H8Vfa2YjdSlpFSetAFFYCuXC04NUGdko7qPbix9ZSdBeiFwMLmzjoxmaHf8CuIrvp8GaXYtlulFXnnzHGvBk0KcJCoRX/3SQoNnVzuqQ1+fwuhuHrnG3yruekf56Pu57PSc7niDnPnJOeNOCccwIAoEQV5Z4AAGBxIiAAABMCAgAwISAAABMCAgAweaXcE/BLPp/Xo0ePtGLFCgUCgXJPBwAWBeecZmZmVF1drYqKZ885lk1AHj16pImJiXJPAwAWpTfeeEOvvfbaM8uWTUBWrFgh6Z9/hKqqqjLPBnhWIpFQNBot9zSA50xPT2tiYqJwDP1fyyYgTy9bVVVVKRgMlnk2wPP4f4mF7L8u/fMhOgDAhIAAAEwICADAhIAAAEzmDMjU1JT27dun1tZW7dixQ/v371c6nZYkNTY2aseOHero6FBHR4fGx8cLj/v555/V1tam7du366OPPtLff//t6RgAwF9zBiQQCGjv3r26dOmSfvjhB73++us6ceJEYXxoaEjnzp3TuXPn1NjYKOmfey6++OILnTp1SleuXFF1dbW+/fZbz8YAAP6bMyChUEhbtmwp/Lx582Ylk8lZH/Prr78qGo1qw4YNkqSuri79+OOPno0BAPxX0n0g+XxeZ86cUUtLS2HZ7t27lcvl9Pbbb6unp0dVVVVKpVKqr68vrFNfX69UKiVJnoyVIpFIlPwYoFTvv/++bt686ek2Nm7cqO+++87TbQCzKSkgR44c0apVq7Rr1y5J0i+//KJwOKy//vpLn3zyiQYHB/Xxxx97MtGXJRqNcsMWPHfjxo2S1g8EAuKPg2IhymazL3zjXfRvYfX39+vOnTv6+uuvC1+oFQ6HJUmvvvqq3nvvPY2OjhaW/+9lrmQyWVjXizEAgP+KCsjJkyeVSCQ0ODhY+B6pP/74Q5lMRpL05MkTXbp0SZFIRJL01ltv6ffff9ft27cl/fNB+7vvvuvZGADAfwE3x3nz9evX1d7erg0bNmjlypWSpPXr12vv3r06dOiQAoGAnjx5ojfffFOfffaZqqurJUk//fSTBgYGlM/nFYlEdPz4ca1atcqzsbk8PQ3jEhYWIi5hYaGa7dg5Z0CWCgKChYyAYKGa7djJnegAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwmTMgU1NT2rdvn1pbW7Vjxw7t379f6XRaknTt2jXt3LlTra2t2rNnjyYnJwuP83sMAOAzN4epqSn322+/FX4+fvy4+/TTT10ul3Pbtm1zIyMjzjnnBgcHXW9vr3PO+T5WjEwm42KxmMtkMkU/BvBLES9FoCxmO3bOeQYSCoW0ZcuWws+bN29WMplUIpFQMBhUc3OzJKmrq0sXL16UJN/HAAD+K+kzkHw+rzNnzqilpUWpVEr19fWFsbq6OuXzeT148MD3MQCA/14pZeUjR45o1apV2rVrl65cueLVnDyVSCTKPQXgP8Xj8XJPAShJ0QHp7+/XnTt3dOrUKVVUVCgcDiuZTBbG0+m0KioqFAqFfB8rRTQaVTAYLOkxgB+amprKPQXgOdls9oVvvIu6hHXy5EklEgkNDg6qqqpK0j8H4kwmo1gsJkkaGhpSW1tbWcYAAP4LOOfcbCtcv35d7e3t2rBhg1auXClJWr9+vQYHBzU6Oqq+vj5ls1k1NDRoYGBAa9askSTfx+bytKKcgWAhCgQCmuOlCJTFbMfOOQOyVBAQLGQEBAvVbMdO7kQHAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgUFZD+/n61tLSosbFRExMTheUtLS1qa2tTR0eHOjo6dPXq1cLYtWvXtHPnTrW2tmrPnj2anJz0dAwA4DNXhJGREZdMJt3WrVvd+Ph4Yfm/f34ql8u5bdu2uZGREeecc4ODg663t9ezsWJkMhkXi8VcJpMp+jGAX4p8KQK+m+3YWdQZSHNzs8LhcNFRSiQSCgaDam5uliR1dXXp4sWLno0BAPz3ynyf4ODBg3LOqampSQcOHFBNTY1SqZTq6+sL69TV1Smfz+vBgweejIVCoaLnm0gk5rnHgDfi8Xi5pwCUZF4BOX36tMLhsKanp3Xs2DEdPnxYJ06ceFlz80Q0GlUwGCz3NIDnNDU1lXsKwHOy2ewL33jP67ewnl7WqqqqUnd3t0ZHRwvLk8lkYb10Oq2KigqFQiFPxgAA/jMH5PHjx3r48KEkyTmnCxcuKBKJSPrnXX4mk1EsFpMkDQ0Nqa2tzbMxAID/As45N9dKR48e1eXLl3X//n3V1tYqFArp1KlT6unpUS6XUz6f16ZNm/T5559r7dq1kqTR0VH19fUpm82qoaFBAwMDWrNmjWdjc3l6GsYlLCxEgUBARbwUAd/NduwsKiBLAQHBQkZAsFDNduzkTnQAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAQCYzPsvEgJLXV1dnaampjzfTiAQ8PT5a2trlU6nPd0GlhcCAsxhamrK82/Kjcfjnv9FQq8DheWHS1gAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwISAAABMCAgAwmTMg/f39amlpUWNjoyYmJgrLb926pc7OTrW2tqqzs1O3b98u2xgAoAzcHEZGRlwymXRbt2514+PjheW7d+92Z8+edc45d/bsWbd79+6yjRUjk8m4WCzmMplMSY8DiniZzFssFvN8G37sB5ae2Y6dc56BNDc3KxwOP7NscnJSY2Njam9vlyS1t7drbGxM6XTa9zEAQHmY/iZ6KpXSunXrVFlZKUmqrKzU2rVrlUql5Jzzdayurm7e/wgAgNKZArKYJRKJck8Bi1A8HmcbwL+YAhIOh3X37l3lcjlVVlYql8vp3r17CofDcs75OlaqaDSqYDBo2W0sY01NTZ4+fzwe93wbkvf7gaUnm82+8I23KSCrV69WJBLR8PCwOjo6NDw8rEgkUric5PcY4KUfe97RzWP/5+k2aiXdvOjpJvRjzzvebgDLTsA552Zb4ejRo7p8+bLu37+v2tpahUIhnT9/Xjdu3FBvb6/+/PNP1dTUqL+/Xxs3bpQk38eK8bSinIGgVIFAQHO8TObNjzMQP/YDS89sx845A7JUEBBYERAsZ7MdO7kTHQBgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACYEBABgQkAAACbzDkhLS4va2trU0dGhjo4OXb16VZJ07do17dy5U62trdqzZ48mJycLj/FiDADgMzdPW7dudePj488sy+Vybtu2bW5kZMQ559zg4KDr7e31bKwYmUzGxWIxl8lk5rG3WI5ewstkTrFYzPNt+LEfWHpmO3Z6cgkrkUgoGAyqublZktTV1aWLFy96NgYA8N8rL+NJDh48KOecmpqadODAAaVSKdXX1xfG6+rqlM/n9eDBA0/GQqHQy9gNAEAJ5h2Q06dPKxwOa3p6WseOHdPhw4e1ffv2lzE3TyQSiXJPAYtQPB5nG8C/zDsg4XBYklRVVaXu7m59+OGH+uCDD5RMJgvrpNNpVVRUKBQKKRwOv/SxUkSjUQWDQevuYplqamry9Pnj8bjn25C83w8sPdls9oVvvOf1Gcjjx4/18OFDSZJzThcuXFAkElE0GlUmk1EsFpMkDQ0Nqa2tTZI8GQMA+G9eZyCTk5Pq6elRLpdTPp/Xpk2b1NfXp4qKCn311Vfq6+tTNptVQ0ODBgYGJMmTMQCA/wLOOVfuSfjh6WkYl7BQqkAgIK9fJn5cwvJjP7D0zHbs5E50AIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIAJAQEAmBAQAIDJogvIrVu31NnZqdbWVnV2dur27dvlnhIALEuLLiB9fX3q7u7WpUuX1N3drUOHDpV7SgCwLC2qgExOTmpsbEzt7e2SpPb2do2NjSmdTpd5ZgCw/LxS7gmUIpVKad26daqsrJQkVVZWau3atUqlUqqrqyvqORKJhJdTxBIVCATKPYV5q6mpUTweL/c0sIQsqoC8DNFoVMFgsNzTwCLinPN8G/F4XE1NTZ5vByhVNpt94RvvRXUJKxwO6+7du8rlcpKkXC6ne/fuKRwOl3lmALD8LKqArF69WpFIRMPDw5Kk4eFhRSKRoi9fAQBenkV3CevLL79Ub2+vvvnmG9XU1Ki/v7/cUwKAZWnRBWTTpk36/vvvyz0NAFj2FtUlLADAwkFAAAAmBAQAYLLoPgOxevq7/NPT02WeCfDfstlsuacAPOfpMfO/7odaNgGZmZmRJE1MTJR5JsB/41sSsJDNzMxo5cqVzywLOD9us10A8vm8Hj16pBUrViyJr6UAAD845zQzM6Pq6mpVVDz7qceyCQgA4OXiQ3QAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAQCYEBAAgAkBAcqov79fLS0tamxs5FsSsOgQEKCM3nnnHZ0+fVoNDQ3lngpQsmXzXVjAQtTc3FzuKQBmnIEAAEwICADAhIAAAEwICADAhK9zB8ro6NGjunz5su7fv6/a2lqFQiGdP3++3NMCikJAAAAmXMICAJgQEACACQEBAJgQEACACQEBAJgQEACACQEBAJgQEACAyf8DTx+QmqCLu48AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "e= df['EstimatedSalary']\n",
        "e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAbumaTC47IW",
        "outputId": "b773aa2d-8302-4732-9cc9-70e3384368cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       101348.88\n",
              "1       112542.58\n",
              "2       113931.57\n",
              "3        93826.63\n",
              "4        79084.10\n",
              "          ...    \n",
              "9995     96270.64\n",
              "9996    101699.77\n",
              "9997     42085.58\n",
              "9998     92888.52\n",
              "9999     38190.78\n",
              "Name: EstimatedSalary, Length: 9986, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(e) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "pq7Rrwn_5BLO",
        "outputId": "4f1080eb-19f9-4931-8346-411ad56f1211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = data['Age']\n",
        "plt.boxplot(a)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "zm0g0Yjo5F2g",
        "outputId": "86be0d9f-3bf1-4cf7-8e2c-a7b6ed8939be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ageOutliers =  np.where(df['Age'] > 60)\n",
        "ageOutliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cxj4-_x65LWl",
        "outputId": "1661fc86-e1bb-4bb2-fd5f-0fd91bed7774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  41,   43,   57,   84,  103,  157,  180,  229,  233,  242,  251,\n",
              "         275,  309,  363,  370,  384,  386,  398,  415,  483,  537,  558,\n",
              "         560,  566,  601,  611,  616,  629,  657,  677,  695,  735,  765,\n",
              "         768,  806,  810,  822,  858,  883,  887,  920,  927,  946,  950,\n",
              "         955,  961,  967,  995, 1007, 1037, 1038, 1053, 1112, 1116, 1190,\n",
              "        1202, 1231, 1232, 1243, 1249, 1275, 1282, 1325, 1339, 1384, 1403,\n",
              "        1406, 1429, 1435, 1453, 1515, 1539, 1584, 1603, 1610, 1637, 1785,\n",
              "        1805, 1852, 1860, 1895, 1898, 1901, 1927, 1974, 1989, 1995, 2005,\n",
              "        2032, 2046, 2071, 2087, 2096, 2101, 2147, 2152, 2157, 2237, 2254,\n",
              "        2267, 2291, 2294, 2426, 2431, 2451, 2452, 2511, 2512, 2525, 2533,\n",
              "        2545, 2590, 2606, 2650, 2661, 2704, 2708, 2751, 2763, 2768, 2769,\n",
              "        2772, 2782, 2846, 2868, 2892, 2899, 2916, 2917, 2999, 3024, 3045,\n",
              "        3101, 3133, 3157, 3183, 3194, 3220, 3296, 3299, 3302, 3305, 3308,\n",
              "        3337, 3357, 3359, 3369, 3373, 3375, 3378, 3387, 3394, 3425, 3453,\n",
              "        3488, 3490, 3518, 3522, 3532, 3540, 3550, 3554, 3564, 3566, 3584,\n",
              "        3593, 3632, 3637, 3638, 3642, 3681, 3682, 3693, 3710, 3719, 3724,\n",
              "        3752, 3765, 3804, 3817, 3871, 3872, 3879, 3900, 3901, 3918, 3931,\n",
              "        3938, 3971, 3985, 4001, 4016, 4039, 4042, 4086, 4133, 4138, 4148,\n",
              "        4153, 4161, 4232, 4235, 4247, 4264, 4271, 4288, 4304, 4309, 4326,\n",
              "        4351, 4357, 4369, 4378, 4387, 4426, 4429, 4454, 4481, 4482, 4492,\n",
              "        4497, 4550, 4554, 4581, 4586, 4635, 4669, 4689, 4738, 4742, 4792,\n",
              "        4806, 4823, 4840, 4922, 4938, 4957, 4983, 4991, 5011, 5024, 5029,\n",
              "        5059, 5123, 5127, 5139, 5150, 5188, 5214, 5216, 5226, 5246, 5290,\n",
              "        5304, 5359, 5368, 5396, 5430, 5448, 5481, 5499, 5505, 5511, 5567,\n",
              "        5568, 5572, 5630, 5642, 5646, 5651, 5655, 5662, 5674, 5689, 5733,\n",
              "        5768, 5774, 5808, 5816, 5831, 5858, 5898, 5948, 5987, 6037, 6107,\n",
              "        6143, 6157, 6158, 6162, 6164, 6203, 6221, 6269, 6280, 6306, 6348,\n",
              "        6357, 6364, 6366, 6401, 6434, 6506, 6521, 6523, 6572, 6603, 6617,\n",
              "        6697, 6700, 6706, 6712, 6750, 6754, 6803, 6890, 6961, 6988, 6999,\n",
              "        7048, 7049, 7054, 7062, 7069, 7085, 7129, 7130, 7133, 7147, 7185,\n",
              "        7193, 7229, 7234, 7263, 7293, 7353, 7366, 7383, 7490, 7505, 7514,\n",
              "        7517, 7539, 7543, 7614, 7615, 7620, 7659, 7678, 7683, 7685, 7700,\n",
              "        7706, 7710, 7711, 7718, 7764, 7767, 7775, 7779, 7793, 7804, 7842,\n",
              "        7885, 7889, 7900, 7924, 7947, 7986, 8010, 8028, 8085, 8089, 8146,\n",
              "        8160, 8183, 8197, 8205, 8207, 8294, 8311, 8375, 8384, 8434, 8448,\n",
              "        8457, 8459, 8468, 8478, 8552, 8558, 8567, 8592, 8664, 8676, 8679,\n",
              "        8701, 8748, 8750, 8751, 8756, 8775, 8781, 8810, 8853, 8888, 8905,\n",
              "        8918, 8958, 9006, 9009, 9050, 9068, 9090, 9100, 9104, 9150, 9162,\n",
              "        9211, 9249, 9267, 9272, 9280, 9297, 9306, 9309, 9312, 9320, 9321,\n",
              "        9339, 9367, 9378, 9389, 9412, 9415, 9425, 9459, 9477, 9493, 9542,\n",
              "        9544, 9569, 9574, 9576, 9580, 9582, 9632, 9657, 9659, 9667, 9672,\n",
              "        9674, 9704, 9719, 9720, 9722, 9733, 9739, 9751, 9818, 9865, 9880,\n",
              "        9883, 9922]),)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "da = data[data['Age'] <=60 ]\n",
        "da"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "ARPlfAUp5Tlt",
        "outputId": "ec0640cb-0c9c-445e-f2eb-7080f3e42dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "0             1    15634602   Hargrave          619    France  Female   42   \n",
              "1             2    15647311       Hill          608     Spain  Female   41   \n",
              "2             3    15619304       Onio          502    France  Female   42   \n",
              "3             4    15701354       Boni          699    France  Female   39   \n",
              "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
              "...         ...         ...        ...          ...       ...     ...  ...   \n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
              "9997       9998    15584532        Liu          709    France  Female   36   \n",
              "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
              "9999      10000    15628319     Walker          792    France  Female   28   \n",
              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0          2       0.00              1          1               1   \n",
              "1          1   83807.86              1          0               1   \n",
              "2          8  159660.80              3          1               0   \n",
              "3          1       0.00              2          0               0   \n",
              "4          2  125510.82              1          1               1   \n",
              "...      ...        ...            ...        ...             ...   \n",
              "9995       5       0.00              2          1               0   \n",
              "9996      10   57369.61              1          1               1   \n",
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        "7.Check for Categorical columns and perform encoding."
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              "      <th>9995</th>\n",
              "      <td>9996</td>\n",
              "      <td>15606229</td>\n",
              "      <td>Obijiaku</td>\n",
              "      <td>771</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96270.64</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>9997</td>\n",
              "      <td>15569892</td>\n",
              "      <td>Johnstone</td>\n",
              "      <td>516</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101699.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>9998</td>\n",
              "      <td>15584532</td>\n",
              "      <td>Liu</td>\n",
              "      <td>709</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>36</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42085.58</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9999</td>\n",
              "      <td>15682355</td>\n",
              "      <td>Sabbatini</td>\n",
              "      <td>772</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Male</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92888.52</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>10000</td>\n",
              "      <td>15628319</td>\n",
              "      <td>Walker</td>\n",
              "      <td>792</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1edf8006-e8ab-48e2-90b2-fd595b80bd44')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1edf8006-e8ab-48e2-90b2-fd595b80bd44 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1edf8006-e8ab-48e2-90b2-fd595b80bd44');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.api.types import is_string_dtype\n",
        "continuous=[]\n",
        "categorical=[]\n",
        "for data1 in data:\n",
        "    if is_string_dtype(data[data1]):\n",
        "        categorical.append(data1)\n",
        "    else:\n",
        "        continuous.append(data1)\n",
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXW-zySsiD8w",
        "outputId": "1c6fb1d2-8065-4c9d-c95d-dd549b027806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Surname', 'Geography', 'Gender']"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8.Split the data into dependent and independent variables."
      ],
      "metadata": {
        "id": "acsT_mKmf4UH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:, 0:1].values\n",
        "y = data.iloc[:, 1]\n",
        "print(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCmlPPlTjrSI",
        "outputId": "a735a0d5-aa2f-4a69-90de-226490f65b7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[    1]\n",
            " [    2]\n",
            " [    3]\n",
            " ...\n",
            " [ 9998]\n",
            " [ 9999]\n",
            " [10000]]\n",
            "0       15634602\n",
            "1       15647311\n",
            "2       15619304\n",
            "3       15701354\n",
            "4       15737888\n",
            "          ...   \n",
            "9995    15606229\n",
            "9996    15569892\n",
            "9997    15584532\n",
            "9998    15682355\n",
            "9999    15628319\n",
            "Name: CustomerId, Length: 10000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "9.Scale the independent variables"
      ],
      "metadata": {
        "id": "FQnCzCoqf9fg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(x)\n",
        "X_test = sc.transform(x)"
      ],
      "metadata": {
        "id": "Y1ctU6lInSJx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "10.Split the data into training and testing"
      ],
      "metadata": {
        "id": "-IhWwk-egAxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test=train_test_split(data,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "vJEA9utJk5HX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AbyiradlKqw",
        "outputId": "c4e2586c-c6db-44e7-e7ca-31bbfb915500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
            "9124       9125    15753639     Gibson          608    France    Male   37   \n",
            "7117       7118    15765257       Meng          564     Spain    Male   31   \n",
            "4955       4956    15581525     Walker          775   Germany    Male   33   \n",
            "5016       5017    15687903    Okonkwo          501    France  Female   29   \n",
            "139         140    15640905      Vasin          579     Spain  Female   35   \n",
            "...         ...         ...        ...          ...       ...     ...  ...   \n",
            "1925       1926    15757931       Fang          804    France    Male   24   \n",
            "4909       4910    15787258       Ross          596     Spain  Female   29   \n",
            "9083       9084    15711396  Henderson          427     Spain    Male   40   \n",
            "6024       6025    15568834    Howells          698     Spain    Male   27   \n",
            "2181       2182    15738605    Fischer          634   Germany  Female   46   \n",
            "\n",
            "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
            "9124       5  146093.39              2          0               0   \n",
            "7117       5  121461.87              1          1               1   \n",
            "4955       3   83501.66              2          1               0   \n",
            "5016       8       0.00              2          1               0   \n",
            "139        1  129490.36              2          0               1   \n",
            "...      ...        ...            ...        ...             ...   \n",
            "1925       3       0.00              2          1               0   \n",
            "4909       6       0.00              2          1               0   \n",
            "9083       8       0.00              2          1               1   \n",
            "6024       6  125427.37              2          0               0   \n",
            "2181       5  123642.36              1          1               1   \n",
            "\n",
            "      EstimatedSalary  Exited  \n",
            "9124        160593.41       0  \n",
            "7117         20432.09       1  \n",
            "4955        128841.31       0  \n",
            "5016        112664.24       0  \n",
            "139           8590.83       1  \n",
            "...               ...     ...  \n",
            "1925        173195.33       0  \n",
            "4909        116696.77       0  \n",
            "9083         82870.75       0  \n",
            "6024         27654.44       0  \n",
            "2181         49725.16       1  \n",
            "\n",
            "[8000 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwJD3D_glQOf",
        "outputId": "3da570e6-f8fb-4f51-c144-391b7071db3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      RowNumber  CustomerId         Surname  CreditScore Geography  Gender  \\\n",
            "5607       5608    15771728       Mackenzie          641   Germany    Male   \n",
            "2309       2310    15621210          Angelo          599   Germany    Male   \n",
            "5962       5963    15571193        Morrison          579   Germany    Male   \n",
            "2728       2729    15770420          Dillon          749   Germany    Male   \n",
            "5800       5801    15610379  Barclay-Harvey          599    France    Male   \n",
            "...         ...         ...             ...          ...       ...     ...   \n",
            "7983       7984    15628272           Singh          774    France  Female   \n",
            "3720       3721    15788775           Milne          473   Germany    Male   \n",
            "802         803    15681554           Alley          614   Germany  Female   \n",
            "4487       4488    15614365        Lombardi          696   Germany    Male   \n",
            "8638       8639    15587573     Castiglione          626   Germany    Male   \n",
            "\n",
            "      Age  Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
            "5607   41       7  104405.54              3          1               0   \n",
            "2309   46       9  123444.72              1          1               1   \n",
            "5962   42       0  144386.32              1          1               1   \n",
            "2728   46      10   78136.36              2          1               1   \n",
            "5800   30       9  105443.68              1          1               1   \n",
            "...   ...     ...        ...            ...        ...             ...   \n",
            "7983   36       9  114997.42              1          1               0   \n",
            "3720   40       8  152576.25              2          1               0   \n",
            "802    31       7  120599.38              2          1               1   \n",
            "4487   31       3  150604.52              1          0               0   \n",
            "8638   27       4  115084.53              2          0               1   \n",
            "\n",
            "      EstimatedSalary  Exited  \n",
            "5607         17384.21       0  \n",
            "2309         31368.08       1  \n",
            "5962         22497.10       1  \n",
            "2728         73470.98       0  \n",
            "5800        121124.53       0  \n",
            "...               ...     ...  \n",
            "7983         75304.09       0  \n",
            "3720         73073.68       0  \n",
            "802          46163.44       0  \n",
            "4487          5566.60       0  \n",
            "8638         26907.43       0  \n",
            "\n",
            "[2000 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyVyUkywlbKw",
        "outputId": "f28f1ebb-46c1-43d6-9b12-35f538a803a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9124    15753639\n",
            "7117    15765257\n",
            "4955    15581525\n",
            "5016    15687903\n",
            "139     15640905\n",
            "          ...   \n",
            "1925    15757931\n",
            "4909    15787258\n",
            "9083    15711396\n",
            "6024    15568834\n",
            "2181    15738605\n",
            "Name: CustomerId, Length: 8000, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NRKVt6Jlrs_",
        "outputId": "633854ff-03c9-444e-b554-27871885d9c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5607    15771728\n",
            "2309    15621210\n",
            "5962    15571193\n",
            "2728    15770420\n",
            "5800    15610379\n",
            "          ...   \n",
            "7983    15628272\n",
            "3720    15788775\n",
            "802     15681554\n",
            "4487    15614365\n",
            "8638    15587573\n",
            "Name: CustomerId, Length: 2000, dtype: int64\n"
          ]
        }
      ]
    }
  ]
}