Deep_Learning_Model.ipynb

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        "id": "-EihC8UKgNJJ"
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      "cell_type": "markdown",
      "source": [
        "#deep learning"
      ],
      "metadata": {
        "id": "_SY8NjarhSmk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
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        "id": "DpUTyhOIhWcq"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df=pd.read_csv('/content/Churn_Modelling.csv')"
      ],
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      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.corr()"
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              "                 RowNumber  CustomerId  CreditScore       Age    Tenure  \\\n",
              "RowNumber         1.000000    0.004202     0.005840  0.000783 -0.006495   \n",
              "CustomerId        0.004202    1.000000     0.005308  0.009497 -0.014883   \n",
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              "NumOfProducts     0.007246    0.016972     0.012238 -0.030680  0.013444   \n",
              "HasCrCard         0.000599   -0.014025    -0.005458 -0.011721  0.022583   \n",
              "IsActiveMember    0.012044    0.001665     0.025651  0.085472 -0.028362   \n",
              "EstimatedSalary  -0.005988    0.015271    -0.001384 -0.007201  0.007784   \n",
              "Exited           -0.016571   -0.006248    -0.027094  0.285323 -0.014001   \n",
              "\n",
              "                  Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
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              "Age              0.028308      -0.030680  -0.011721        0.085472   \n",
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              "EstimatedSalary         1.000000  0.012097  \n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
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              "array([[619, 'France', 'Female', ..., 1, 1, 101348.88],\n",
              "       [608, 'Spain', 'Female', ..., 0, 1, 112542.58],\n",
              "       [502, 'France', 'Female', ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [709, 'France', 'Female', ..., 0, 1, 42085.58],\n",
              "       [772, 'Germany', 'Male', ..., 1, 0, 92888.52],\n",
              "       [792, 'France', 'Female', ..., 1, 0, 38190.78]], dtype=object)"
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              "array(['France', 'Germany', 'Spain'], dtype=object)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x88YLx9LiUw7",
        "outputId": "6d077628-f3bc-4f29-bbb8-9414f9e4deb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Female', 'Male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "Fo3SQ-B8iUz0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le=LabelEncoder()"
      ],
      "metadata": {
        "id": "QfA1orbZi0tY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x[:,1]=le.fit_transform(x[:,1])"
      ],
      "metadata": {
        "id": "a2qdgKfQi03F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(x[:,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiS1qdOri06b",
        "outputId": "407fd66f-c7de-4aa8-b84e-5e48c55e4671"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[:,2]=le.fit_transform(x[:,2])"
      ],
      "metadata": {
        "id": "pbzD3i69i0-K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(x[:,2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uuBGG6ei1Bo",
        "outputId": "54c90e86-81c1-4b55-8329-1d086ad3c6a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "Q7FVHIhkoLq5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sc=StandardScaler()"
      ],
      "metadata": {
        "id": "G8G1B-yzoL3C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1=sc.fit_transform(x)"
      ],
      "metadata": {
        "id": "ZoVQh6r9oL6S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REI-hBs_oq_R",
        "outputId": "dc34c2ff-9749-4c17-89a7-bddc357b2e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "RTflh4oTorCl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x1,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "sQ9qmY8dorLg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras"
      ],
      "metadata": {
        "id": "qr6ENvLEorQI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "3tc3ECI-orZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier=Sequential()"
      ],
      "metadata": {
        "id": "Jt8VTZUMqZ7d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.add(Dense(units=6,kernel_initializer='uniform',activation='relu'))\n",
        "classifier.add(Dense(units=6,kernel_initializer='uniform',activation='relu'))\n",
        "classifier.add(Dense(units=1,kernel_initializer='uniform',activation='relu'))\n",
        "classifier.compile(optimizer='adam',metrics=['Accuracy'],loss='binary_crossentropy')\n",
        "classifier.fit(x_train,y_train,batch_size=10,epochs=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19hzSRLiqaAp",
        "outputId": "1832144f-7e13-4c23-b369-faf8c4db0d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 2/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 3/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 4/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 5/15\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 6/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 7/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 8/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 9/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 10/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 11/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 12/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 13/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 14/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n",
            "Epoch 15/15\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 3.1544 - Accuracy: 0.7955\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efeb760e790>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=classifier.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSnEJQmJqaHp",
        "outputId": "345be89f-c2ec-46d4-b106-a72bc388490a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KR8aXuZ9utH7",
        "outputId": "b3b1c42e-d3bd-4779-8fc5-0edb149990ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.4275543e-09]\n",
            " [3.0542396e-10]\n",
            " [2.2313467e-09]\n",
            " ...\n",
            " [5.7529947e-10]\n",
            " [8.0511331e-10]\n",
            " [7.2461273e-11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=(y_pred>0.5)"
      ],
      "metadata": {
        "id": "QI2TiXf5utQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbU7etvWutTa",
        "outputId": "e1f1dadb-e7eb-4573-f4e2-0e973b868d30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False],\n",
              "       [False],\n",
              "       [False],\n",
              "       ...,\n",
              "       [False],\n",
              "       [False],\n",
              "       [False]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "eCt5EXPLvlAD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DF8s4kwovlJo",
        "outputId": "8dfb9498-27b5-4e1b-a89b-bc43ed7228d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1599    0]\n",
            " [ 401    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UeXPO6uvlNp",
        "outputId": "d8efc452-fa27-4360-e00c-27c72e2b08ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1599/2000*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ubo_4VfDvlR4",
        "outputId": "98175420-8fcf-49ca-a121-9e14dc002dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "79.95"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    }
  ]
}