Assigment3_ANN.ipynb

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          "base_uri": "https://localhost:8080/",
          "height": 73
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        {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "  return element;\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
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              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
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              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
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              " * @param {string} outputId Element ID of the output display.\n",
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              " */\n",
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              "\n",
              "  for (const file of files) {\n",
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              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
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              "                                    'n/a'} - `));\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Churn_Modelling.csv to Churn_Modelling.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv('Churn_Modelling.csv', index_col = 'RowNumber')\n",
        "\n",
        "# Label Encoding for 'Geography' and 'Gender\n",
        "label_encoder = LabelEncoder()\n",
        "df['Geography'] = label_encoder.fit_transform(df['Geography'])\n",
        "df['Gender'] = label_encoder.fit_transform(df['Gender'])\n",
        "\n",
        "\n",
        "\n",
        "# Selecting features and target variable\n",
        "X = df.drop(['CustomerId', 'Surname','Exited'], axis=1)  # Assuming 'Exited' is the target variable\n",
        "y = df['Exited']\n",
        "\n",
        "# Encoding categorical variables and scaling numerical variables\n",
        "categorical_features = ['Gender','Geography']  # Update with actual categorical features\n",
        "numerical_features = [col for col in X.columns if col not in categorical_features]\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numerical_features),\n",
        "        ('cat', OneHotEncoder(), categorical_features)\n",
        "    ])\n",
        "\n",
        "X_processed = preprocessor.fit_transform(X)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Neural Network Creation\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(12,input_dim = X_processed.shape[1],activation='relu'),\n",
        "    tf.keras.layers.Dense(8,activation='relu'),\n",
        "    tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=20, batch_size=10)\n",
        "\n",
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f'Accuracy: {accuracy*100}%')\n"
      ],
      "metadata": {
        "id": "Y5APTpKX7Wsk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a245efc2-42f7-4459-c006-080af033ac03"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "800/800 [==============================] - 5s 4ms/step - loss: 0.4858 - accuracy: 0.7720\n",
            "Epoch 2/20\n",
            "800/800 [==============================] - 3s 3ms/step - loss: 0.3882 - accuracy: 0.8350\n",
            "Epoch 3/20\n",
            "800/800 [==============================] - 2s 3ms/step - loss: 0.3631 - accuracy: 0.8446\n",
            "Epoch 4/20\n",
            "800/800 [==============================] - 4s 5ms/step - loss: 0.3534 - accuracy: 0.8545\n",
            "Epoch 5/20\n",
            "800/800 [==============================] - 3s 4ms/step - loss: 0.3473 - accuracy: 0.8555\n",
            "Epoch 6/20\n",
            "800/800 [==============================] - 5s 6ms/step - loss: 0.3438 - accuracy: 0.8571\n",
            "Epoch 7/20\n",
            "800/800 [==============================] - 2s 3ms/step - loss: 0.3411 - accuracy: 0.8579\n",
            "Epoch 8/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3402 - accuracy: 0.8594\n",
            "Epoch 9/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3390 - accuracy: 0.8586\n",
            "Epoch 10/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3388 - accuracy: 0.8602\n",
            "Epoch 11/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3375 - accuracy: 0.8621\n",
            "Epoch 12/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3373 - accuracy: 0.8614\n",
            "Epoch 13/20\n",
            "800/800 [==============================] - 2s 3ms/step - loss: 0.3361 - accuracy: 0.8606\n",
            "Epoch 14/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3351 - accuracy: 0.8625\n",
            "Epoch 15/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3346 - accuracy: 0.8626\n",
            "Epoch 16/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3341 - accuracy: 0.8627\n",
            "Epoch 17/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3329 - accuracy: 0.8620\n",
            "Epoch 18/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3330 - accuracy: 0.8636\n",
            "Epoch 19/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3314 - accuracy: 0.8609\n",
            "Epoch 20/20\n",
            "800/800 [==============================] - 2s 3ms/step - loss: 0.3317 - accuracy: 0.8625\n",
            "63/63 [==============================] - 0s 2ms/step - loss: 0.3348 - accuracy: 0.8635\n",
            "Accuracy: 86.34999990463257%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEszqMTQAi1p",
        "outputId": "9d190f2a-a15a-47dc-89be-d13f11f9ecc4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_13 (Dense)            (None, 12)                168       \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 8)                 104       \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 281 (1.10 KB)\n",
            "Trainable params: 281 (1.10 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Generate predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred = [1 if y > 0.5 else 0 for y in y_pred]  # Convert probabilities to class labels\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSjkS9v3DI2C",
        "outputId": "93fa4e0a-4d08-4205-d5cb-a866d3a5ef9b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step\n",
            "Confusion Matrix:\n",
            "[[1542   65]\n",
            " [ 208  185]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OlJsYmYzKajC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}