Day_5_ANN_for_Classification.ipynb

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      "name": "Day 5. ANN for Classification.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "Dataset : https://drive.google.com/file/d/1rxj60Mm2_gWcDqNldS3x38Ue8opQcuR6/view?usp=sharing"
      ],
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        "id": "2Br5rBfGeu7X"
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      "cell_type": "code",
      "source": [
        "# Now we will learn about using ANN for semi-real scenario with business question to create segmentation model which customers have a really high possibility of churn rates\n",
        "# This could be applied in a million of scenarios such as credit approval, insurance, promo approval, voucher, or another things."
      ],
      "metadata": {
        "id": "qbSa5Xz5e0qO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "sheet_url = 'https://drive.google.com/file/d/1rxj60Mm2_gWcDqNldS3x38Ue8opQcuR6/view?usp=sharing'\n",
        "sheet_url ='https://drive.google.com/uc?id=' + sheet_url.split('/')[-2]\n",
        "Bank_Churn = pd.read_csv(sheet_url,encoding= 'unicode_escape')\n",
        "Bank_Churn.head()"
      ],
      "metadata": {
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          "height": 206
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      "outputs": [
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              "      <th></th>\n",
              "      <th>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Hargrave</td>\n",
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              "      <td>France</td>\n",
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              "      <td>15647311</td>\n",
              "      <td>Hill</td>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
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              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
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              "      <td>113931.57</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
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              "      <td>93826.63</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
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            "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",
              "   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",
              "4         79084.10       0  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create the dataset"
      ],
      "metadata": {
        "id": "RNrejKybeIZk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(Bank_Churn.isna().sum())\n",
        "# No null values, nice"
      ],
      "metadata": {
        "id": "HwRLXygifhuG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a48a09e8-591f-4f23-da72-9a7ee963c46d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = Bank_Churn.iloc[:, 3:-1].values\n",
        "y = Bank_Churn.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "QbjH2XjyeXNw"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding the geograohy and gender"
      ],
      "metadata": {
        "id": "EtBAS03Oe7bE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For gender\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "X[:, 2] = le.fit_transform(X[:, 2])"
      ],
      "metadata": {
        "id": "HgWi6tkXeyEm"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For Geography\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "metadata": {
        "id": "FWJW4vAWffDt"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split Dataset"
      ],
      "metadata": {
        "id": "z6ZPC0z-fk-G"
      }
    },
    {
      "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 = 0)"
      ],
      "metadata": {
        "id": "409BoO-hfiQ2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Scaling"
      ],
      "metadata": {
        "id": "3pidUxw0fuZW"
      }
    },
    {
      "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)"
      ],
      "metadata": {
        "id": "lC6x0oBXfvck"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build and Train ANN"
      ],
      "metadata": {
        "id": "wSsCDPbofxik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units=36, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=64, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "-6yJZPVMfyny"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, batch_size = 8, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fclqbuxMisfq",
        "outputId": "953e4a8f-6eb6-44d5-8aa5-e91aa3c998db"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1000/1000 [==============================] - 3s 2ms/step - loss: 0.4343 - accuracy: 0.8115\n",
            "Epoch 2/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3649 - accuracy: 0.8519\n",
            "Epoch 3/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3487 - accuracy: 0.8576\n",
            "Epoch 4/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3426 - accuracy: 0.8601\n",
            "Epoch 5/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3372 - accuracy: 0.8636\n",
            "Epoch 6/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3347 - accuracy: 0.8640\n",
            "Epoch 7/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3323 - accuracy: 0.8639\n",
            "Epoch 8/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3282 - accuracy: 0.8658\n",
            "Epoch 9/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3265 - accuracy: 0.8696\n",
            "Epoch 10/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3228 - accuracy: 0.8691\n",
            "Epoch 11/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3209 - accuracy: 0.8691\n",
            "Epoch 12/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3192 - accuracy: 0.8674\n",
            "Epoch 13/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3176 - accuracy: 0.8673\n",
            "Epoch 14/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3129 - accuracy: 0.8705\n",
            "Epoch 15/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3119 - accuracy: 0.8730\n",
            "Epoch 16/100\n",
            "1000/1000 [==============================] - 3s 3ms/step - loss: 0.3096 - accuracy: 0.8744\n",
            "Epoch 17/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3065 - accuracy: 0.8736\n",
            "Epoch 18/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.3051 - accuracy: 0.8741\n",
            "Epoch 19/100\n",
            "1000/1000 [==============================] - 3s 3ms/step - loss: 0.3017 - accuracy: 0.8746\n",
            "Epoch 20/100\n",
            "1000/1000 [==============================] - 3s 3ms/step - loss: 0.3007 - accuracy: 0.8760\n",
            "Epoch 21/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2991 - accuracy: 0.8775\n",
            "Epoch 22/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2965 - accuracy: 0.8767\n",
            "Epoch 23/100\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.2941 - accuracy: 0.8786\n",
            "Epoch 24/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2932 - accuracy: 0.8789\n",
            "Epoch 25/100\n",
            "1000/1000 [==============================] - 3s 3ms/step - loss: 0.2899 - accuracy: 0.8804\n",
            "Epoch 26/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2885 - accuracy: 0.8779\n",
            "Epoch 27/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2868 - accuracy: 0.8802\n",
            "Epoch 28/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2851 - accuracy: 0.8836\n",
            "Epoch 29/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2835 - accuracy: 0.8842\n",
            "Epoch 30/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2821 - accuracy: 0.8861\n",
            "Epoch 31/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2774 - accuracy: 0.8819\n",
            "Epoch 32/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2786 - accuracy: 0.8855\n",
            "Epoch 33/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2750 - accuracy: 0.8884\n",
            "Epoch 34/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2737 - accuracy: 0.8863\n",
            "Epoch 35/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2725 - accuracy: 0.8864\n",
            "Epoch 36/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2699 - accuracy: 0.8905\n",
            "Epoch 37/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2682 - accuracy: 0.8894\n",
            "Epoch 38/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2673 - accuracy: 0.8896\n",
            "Epoch 39/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2662 - accuracy: 0.8903\n",
            "Epoch 40/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2639 - accuracy: 0.8896\n",
            "Epoch 41/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2635 - accuracy: 0.8930\n",
            "Epoch 42/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2617 - accuracy: 0.8885\n",
            "Epoch 43/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2605 - accuracy: 0.8924\n",
            "Epoch 44/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2594 - accuracy: 0.8921\n",
            "Epoch 45/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2556 - accuracy: 0.8923\n",
            "Epoch 46/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2556 - accuracy: 0.8930\n",
            "Epoch 47/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2535 - accuracy: 0.8966\n",
            "Epoch 48/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2526 - accuracy: 0.8971\n",
            "Epoch 49/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2505 - accuracy: 0.8939\n",
            "Epoch 50/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2489 - accuracy: 0.8954\n",
            "Epoch 51/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2487 - accuracy: 0.8975\n",
            "Epoch 52/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2452 - accuracy: 0.8949\n",
            "Epoch 53/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2475 - accuracy: 0.8966\n",
            "Epoch 54/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2433 - accuracy: 0.8994\n",
            "Epoch 55/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2431 - accuracy: 0.8988\n",
            "Epoch 56/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2404 - accuracy: 0.8997\n",
            "Epoch 57/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2420 - accuracy: 0.8994\n",
            "Epoch 58/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2361 - accuracy: 0.9011\n",
            "Epoch 59/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2374 - accuracy: 0.9015\n",
            "Epoch 60/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2338 - accuracy: 0.9004\n",
            "Epoch 61/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2349 - accuracy: 0.9001\n",
            "Epoch 62/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2327 - accuracy: 0.9031\n",
            "Epoch 63/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2321 - accuracy: 0.9007\n",
            "Epoch 64/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2293 - accuracy: 0.9045\n",
            "Epoch 65/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2299 - accuracy: 0.9040\n",
            "Epoch 66/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2265 - accuracy: 0.9053\n",
            "Epoch 67/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2264 - accuracy: 0.9034\n",
            "Epoch 68/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2273 - accuracy: 0.9024\n",
            "Epoch 69/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2229 - accuracy: 0.9085\n",
            "Epoch 70/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2237 - accuracy: 0.9075\n",
            "Epoch 71/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2222 - accuracy: 0.9072\n",
            "Epoch 72/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2203 - accuracy: 0.9065\n",
            "Epoch 73/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2219 - accuracy: 0.9066\n",
            "Epoch 74/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2158 - accuracy: 0.9141\n",
            "Epoch 75/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2162 - accuracy: 0.9097\n",
            "Epoch 76/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2170 - accuracy: 0.9086\n",
            "Epoch 77/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2163 - accuracy: 0.9072\n",
            "Epoch 78/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2156 - accuracy: 0.9072\n",
            "Epoch 79/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2136 - accuracy: 0.9101\n",
            "Epoch 80/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2131 - accuracy: 0.9096\n",
            "Epoch 81/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2128 - accuracy: 0.9104\n",
            "Epoch 82/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2073 - accuracy: 0.9147\n",
            "Epoch 83/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2093 - accuracy: 0.9112\n",
            "Epoch 84/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2080 - accuracy: 0.9125\n",
            "Epoch 85/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2061 - accuracy: 0.9151\n",
            "Epoch 86/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2038 - accuracy: 0.9137\n",
            "Epoch 87/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2024 - accuracy: 0.9153\n",
            "Epoch 88/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2051 - accuracy: 0.9135\n",
            "Epoch 89/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2025 - accuracy: 0.9114\n",
            "Epoch 90/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2009 - accuracy: 0.9149\n",
            "Epoch 91/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2004 - accuracy: 0.9141\n",
            "Epoch 92/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1966 - accuracy: 0.9171\n",
            "Epoch 93/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1973 - accuracy: 0.9153\n",
            "Epoch 94/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1973 - accuracy: 0.9169\n",
            "Epoch 95/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1964 - accuracy: 0.9186\n",
            "Epoch 96/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1958 - accuracy: 0.9176\n",
            "Epoch 97/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1942 - accuracy: 0.9171\n",
            "Epoch 98/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1917 - accuracy: 0.9183\n",
            "Epoch 99/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1936 - accuracy: 0.9187\n",
            "Epoch 100/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1898 - accuracy: 0.9193\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f775c580a90>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting Test Result"
      ],
      "metadata": {
        "id": "3IsH0WYlix8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYwTd52oi-Pf",
        "outputId": "0be56559-f235-4673-99e0-f9143b26eef0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " ...\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqXECWsEi_5v",
        "outputId": "c537d815-7d2d-4b2a-c60e-9bce54c9939b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1454  141]\n",
            " [ 175  230]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.842"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.predict(sc.transform([[1, 0, 2, 600, 1, 40, 3, 50000, 2, 1, 1, 50000]])) > 0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wS-AkzPfiy1P",
        "outputId": "72e08bfc-e178-49b2-8d2c-b3af35995c1b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[False]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KZBPyJ4oi3tv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}