AnnCLassification.ipynb

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        "<a href=\"https://colab.research.google.com/github/alimoradi296/DeepLearning/blob/main/AnnCLassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#ANN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Z7VzrlXqnw0C"
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      "cell_type": "markdown",
      "source": [
        "##Datapreprocessing Template"
      ],
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      }
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      "cell_type": "markdown",
      "source": [
        "###importing the Libraries"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "cell_type": "markdown",
      "source": [
        "###importing dataset"
      ],
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        "id": "LpUx6b7DpA2S"
      }
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      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/sample_data/Churn_Modelling.csv\")"
      ],
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        "id": "Ofi8GP7z2oj2"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "   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",
              "   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",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
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        "df1=df.drop(columns=['RowNumber','CustomerId','Surname','Exited'])\n",
        "y=df.Exited"
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            "text/plain": [
              "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0             619    France  Female   42       2       0.00              1   \n",
              "1             608     Spain  Female   41       1   83807.86              1   \n",
              "2             502    France  Female   42       8  159660.80              3   \n",
              "3             699    France  Female   39       1       0.00              2   \n",
              "4             850     Spain  Female   43       2  125510.82              1   \n",
              "...           ...       ...     ...  ...     ...        ...            ...   \n",
              "9995          771    France    Male   39       5       0.00              2   \n",
              "9996          516    France    Male   35      10   57369.61              1   \n",
              "9997          709    France  Female   36       7       0.00              1   \n",
              "9998          772   Germany    Male   42       3   75075.31              2   \n",
              "9999          792    France  Female   28       4  130142.79              1   \n",
              "\n",
              "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
              "0             1               1        101348.88  \n",
              "1             0               1        112542.58  \n",
              "2             1               0        113931.57  \n",
              "3             0               0         93826.63  \n",
              "4             1               1         79084.10  \n",
              "...         ...             ...              ...  \n",
              "9995          1               0         96270.64  \n",
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              "9997          0               1         42085.58  \n",
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              "9999          1               0         38190.78  \n",
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              "[10000 rows x 10 columns]"
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              "<p>10000 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a23dcf80-df2b-4774-9d90-6a3cd71345be')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a23dcf80-df2b-4774-9d90-6a3cd71345be 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-a23dcf80-df2b-4774-9d90-6a3cd71345be');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Category Encoding\n"
      ],
      "metadata": {
        "id": "3Ia1IbMovMUt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "MzEKl7A04wjh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le=LabelEncoder()\n",
        "df1.Gender=le.fit_transform(df1.Gender)"
      ],
      "metadata": {
        "id": "1kg-9tKilykx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "tTDpXTY6mpD8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df1.values"
      ],
      "metadata": {
        "id": "HU5s0XcGmGRO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ohEncoder=OneHotEncoder()"
      ],
      "metadata": {
        "id": "cS3aPKWS5PhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columnTransformer=ColumnTransformer([('onehotencoder',ohEncoder,[1])],remainder='passthrough')"
      ],
      "metadata": {
        "id": "m3BJ6pDsgkPW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=np.array(columnTransformer.fit_transform(df2))"
      ],
      "metadata": {
        "id": "xk_WMzEXhuaI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aF1ysG9Hh-W6",
        "outputId": "488f5207-6a34-4c0f-bcb6-d2365a761e91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0, 0.0, 0.0, ..., 1, 1, 101348.88],\n",
              "       [0.0, 0.0, 1.0, ..., 0, 1, 112542.58],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [1.0, 0.0, 0.0, ..., 0, 1, 42085.58],\n",
              "       [0.0, 1.0, 0.0, ..., 1, 0, 92888.52],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 38190.78]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split Train and Test"
      ],
      "metadata": {
        "id": "UY4FcjPlvUdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=15)"
      ],
      "metadata": {
        "id": "H7wYSDm-j9P_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2pdLz-ynY4E",
        "outputId": "c0c058e8-8733-4ff5-faa1-dda1692cc204"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000,)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83o7_vhHnaqQ",
        "outputId": "8f585a2d-98a8-4728-dc59-222dd755b69e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0, 0.0, 0.0, ..., 1, 1, 101348.88],\n",
              "       [0.0, 0.0, 1.0, ..., 0, 1, 112542.58],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [1.0, 0.0, 0.0, ..., 0, 1, 42085.58],\n",
              "       [0.0, 1.0, 0.0, ..., 1, 0, 92888.52],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 38190.78]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature Scaling\n"
      ],
      "metadata": {
        "id": "I5f5komCuyN8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc =StandardScaler()\n",
        "X_train=sc.fit_transform(X_train)\n",
        "X_test=sc.transform(X_test)\n"
      ],
      "metadata": {
        "id": "hzohzBfLvnWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2O5eNuPx0aQ",
        "outputId": "a20a7e7e-55fb-41c3-f448-089b3ff1e9f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.99950012, -0.58196914, -0.57234647, ...,  0.6420125 ,\n",
              "        -1.03227043,  1.00833475],\n",
              "       [-1.00050013, -0.58196914,  1.74719344, ...,  0.6420125 ,\n",
              "        -1.03227043, -0.77300966],\n",
              "       [-1.00050013, -0.58196914,  1.74719344, ..., -1.55760207,\n",
              "        -1.03227043,  1.72489478],\n",
              "       ...,\n",
              "       [ 0.99950012, -0.58196914, -0.57234647, ..., -1.55760207,\n",
              "        -1.03227043, -1.67575833],\n",
              "       [ 0.99950012, -0.58196914, -0.57234647, ...,  0.6420125 ,\n",
              "        -1.03227043, -1.20846462],\n",
              "       [ 0.99950012, -0.58196914, -0.57234647, ...,  0.6420125 ,\n",
              "         0.9687384 ,  0.55450141]])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building the ANN\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MmDXF7VAx17m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### import libraries"
      ],
      "metadata": {
        "id": "UrksV87T3K3a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "P-g-3UX8y7qH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add Layers"
      ],
      "metadata": {
        "id": "0m6NUw4i3YCO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential()\n",
        "model.add(keras.layers.Dense(units=6,activation='relu'))\n",
        "model.add(keras.layers.Dense(units=8,activation='relu'))\n",
        "model.add(keras.layers.Dense(units=1,activation='sigmoid'))\n"
      ],
      "metadata": {
        "id": "06IOP6b73g8J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the Model"
      ],
      "metadata": {
        "id": "WhSZlOb14Dgg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compiling Model\n"
      ],
      "metadata": {
        "id": "k49r0EOM4T_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.0001),loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "aY4WuR3j4OTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the Model"
      ],
      "metadata": {
        "id": "rmxRfVeV5KQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,batch_size=32,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukkNTy3O6MBa",
        "outputId": "4326c7f9-3f8e-4dc5-f9b2-9e4d5c9ec7d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.9260 - accuracy: 0.2453\n",
            "Epoch 2/50\n",
            "130/250 [==============>...............] - ETA: 0s - loss: 0.7939 - accuracy: 0.3481"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predictiong and Evaluting"
      ],
      "metadata": {
        "id": "VKz-l4Tp6Wrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=(model.predict(X_test)>0.5)"
      ],
      "metadata": {
        "id": "qXeBpIRa6zgl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making the  Confusion Matrix\n"
      ],
      "metadata": {
        "id": "n55z0iyk642o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "c1PDGVh2_h_k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c2e5726r_vdh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}