Email_Spam_Detection_with_Machine_Learning.ipynb

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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
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              "True"
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      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set_style(\"white\")\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "from pickle import dump\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FYGGE1xrfVv"
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      "source": [
        "### **Project Pipeline**\n",
        "For any machine learning project it consist of three main phases as following :-\n",
        "1. **Scoping :** List the problem description and project goals\n",
        "2. **The Data :** Load, analyse and prepare the dataset for training\n",
        "3. **The Model :** Create and train the model on the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBjFjoTErfVw"
      },
      "source": [
        "## **1 | Scoping**\n",
        "The project problem is that we have a dataset containing a set of emails and we will use machine learning and nlp techniques in order to determine if this email is spam or not."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRy9TJXmrfVx"
      },
      "source": [
        "## **2 | The Data**\n",
        "In this phase we will analyze and prepare the dataset before training by applying varoius steps as following :-\n",
        "1. Data Loading\n",
        "2. Data Visualization\n",
        "3. Data Cleaning\n",
        "4. Data Splitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67t4L-qFrfVy"
      },
      "source": [
        "##### **1. Data Loading**\n",
        "Load the dataset and show its info and statistics"
      ]
    },
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      "source": [
        "# Load the dataset\n",
        "dataset = pd.read_csv('/content/spam.csv', encoding=\"ISO-8859-1\")\n",
        "dataset.shape"
      ]
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              "     v1                                                 v2 Unnamed: 2  \\\n",
              "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
              "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
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              "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
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            " 3   Unnamed: 3  12 non-null     object\n",
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            "dtypes: object(5)\n",
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        "# Show dataset info\n",
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              "          v1                      v2  \\\n",
              "count   5572                    5572   \n",
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              "top      ham  Sorry, I'll call later   \n",
              "freq    4825                      30   \n",
              "\n",
              "                                               Unnamed: 2  \\\n",
              "count                                                  50   \n",
              "unique                                                 43   \n",
              "top      bt not his girlfrnd... G o o d n i g h t . . .@\"   \n",
              "freq                                                    3   \n",
              "\n",
              "                   Unnamed: 3 Unnamed: 4  \n",
              "count                      12          6  \n",
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              "top      MK17 92H. 450Ppw 16\"    GNT:-)\"  \n",
              "freq                        2          2  "
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      "source": [
        "##### **2. Data Visualization**\n",
        "Visualize dataset features frequencies to get some insights"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualize spam  frequenices\n",
        "plt.figure(dpi=100)\n",
        "sns.countplot(dataset['v1'])\n",
        "plt.title(\"Spam Freqencies\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQ9Mmwehuak6"
      },
      "source": [
        "##### **3. Data Cleaning**\n",
        "Handling missing values and check for duplicates "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBieu4tFCgzB",
        "outputId": "ba886751-f938-4a88-b97e-69d9aa759295"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v1               0\n",
              "v2               0\n",
              "Unnamed: 2    5522\n",
              "Unnamed: 3    5560\n",
              "Unnamed: 4    5566\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Check for missing data for each column \n",
        "dataset.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "fMhpL5McDm6C"
      },
      "outputs": [],
      "source": [
        "# Check for duplicates and remove them \n",
        "dataset.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "0wEWbr70Duip"
      },
      "outputs": [],
      "source": [
        "# Cleaning data from punctuation and stopwords and then tokenizing it into words (tokens)\n",
        "def process(text):\n",
        "    nopunc = [char for char in text if char not in string.punctuation]\n",
        "    nopunc = ''.join(nopunc)\n",
        "    clean = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n",
        "    return clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "CCg2EmYND49R"
      },
      "outputs": [],
      "source": [
        "# Fit the CountVectorizer to data\n",
        "message = CountVectorizer(analyzer=process).fit_transform(dataset['v2'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hHioFvvxXZR"
      },
      "source": [
        "##### **4. Data Splitting**\n",
        "Split the dataset into training and testing sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "sd9wSNqAxfEC"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(message, dataset['v1'], test_size=0.20, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpYJbHRLybyT"
      },
      "source": [
        "## **3. The Model**\n",
        "In this phase we will create and train a multinomial naive bayes model "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "MQpjkfO8A__Q"
      },
      "outputs": [],
      "source": [
        "# Model creation\n",
        "model = MultinomialNB()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZGqCECQFm-r",
        "outputId": "64abd529-2eb2-4bad-8273-33964b47ae61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Model training\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qqcSnwI-rfV9"
      },
      "outputs": [],
      "source": [
        "# Model predictions on test set\n",
        "y_pred = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xv8olOoVy2pr",
        "outputId": "2773f430-b1ff-4c26-d82f-5937f5f92e39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.55125725338492"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Model Evaluation | Accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "accuracy * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "rNRGLv3byxDS",
        "outputId": "850c2f1b-7661-4700-b862-540b38bf7be8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         ham       0.99      0.96      0.97       885\\n        spam       0.80      0.93      0.86       149\\n\\n    accuracy                           0.96      1034\\n   macro avg       0.89      0.94      0.92      1034\\nweighted avg       0.96      0.96      0.96      1034\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Model Evaluation | Classification report\n",
        "classification_report(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "lns53eXzzVht",
        "outputId": "a0688c83-5098-4597-9742-03ee2773b915"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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oEHQ6Hby8vKocCytqIiKSDL2+fl7K0bt3byxduhROTk7C1PfKlSsxaNAgAICZmRmGDx+Ozz77DG3atIGzszMWLFgAR0dH9OnTBwDg5uaG559/HjNmzEBcXBzKysoQHx+P/v37o0WLFlWOxUx//93bDahMfbahQyCqc81cXmzoEIjqXFHJuTo79p3M76s9trHPa1XuW1RUhAULFmDXrl3C9Hb//v0xbtw4Yfpcr9dj4cKFSE1NRUFBAXx9fRETEwNXV1fhODdv3kR8fDz27NkDmUyGl19+GdHR0bC0tKxyLEzURPWIiZpMQZ0m6t++q/bYxp3fqMVI6g+nvomISDr4Ug4iIiIRM/IJY08CJmoiIpIOE6yoeXsWERGRiLGiJiIi6TDB91EzURMRkXSY4NQ3EzUREUkHK2oiIiIRY6ImIiISr/p6hKiYMFETEZF0mGBFzduziIiIRIwVNRERSQdXfRMREYmYCU59M1ETEZF0sKImIiISMVbUREREImaCFTVXfRMREYkYK2oiIpIOTn0TERGJGBM1ERGRiJngNWomaiIikg5W1ERERCLGipqIiEjETLCi5u1ZREREIsaKmoiIpMMEp75ZURMRkXTodNXfjBAYGAgPD49KW1xcHACgtLQUcXFx6N69O5RKJcLCwqBWqw2OkZubi9GjR8Pb2xt+fn6YPXs27t69a/RXZkVNRETSUU/XqL/++muUl5cLn0+fPo3g4GD07dsXAJCYmIj9+/dj/vz5sLa2Rnx8PEJDQ7FhwwYAQHl5OUJCQmBvb48NGzYgLy8PkZGRsLCwwMSJE42KhRU1ERFJh15f/c0IzZs3h4ODg7Dt3bsXLi4u6NatGwoLC5GWloaoqCj4+fnB09MTiYmJyMjIQGZmJgDgp59+Qk5ODubOnYsOHTogICAA48ePx7p166DVao2KhYmaiIikowZT31qtFkVFRQZbVZKmVqvFd999h0GDBsHMzAzZ2dkoKyuDv7+/0MfNzQ1OTk5Cos7MzIRCoYC9vb3QR6VSoaioCDk5OUZ9ZSZqIiIyCSkpKfD19TXYUlJSHjtu165dKCwsRFBQEABArVbDwsICNjY2Bv3s7OyQn58v9Lk/SQMQPlf0qSpeoyYiIumowTXqkJAQBAcHG7TJ5fLHjktLS0OvXr3QokWLap+7JlhRExGRdOh11d7kcjmsrKwMtscl6itXruDgwYN46623hDZ7e3uUlZWhoKDAoK9Go4GDg4PQ5++rwCs+V/SpKiZqIiKSjnq6PavCpk2bYGdnhxdeeEFo8/T0hIWFBdLT04W2s2fPIjc3Fz4+PgAAHx8fnDp1ChqNRuhz8OBBWFlZwd3d3agYOPVNRETSYeTq7ZrQ6XTYtGkTBgwYgEaN/pcura2tMWjQICQnJ8PW1hZWVlZISEiAUqkUErVKpYK7uzsiIiIwZcoU5OfnY/78+Rg6dGiVptvvx0RNRETSUY/P+j548CByc3MxaNCgSvumTZsGmUyG8PBwaLVaqFQqxMTECPvNzc2xdOlSxMbGYvDgwWjSpAmCgoIQHh5udBxmen09/nnyCGXqsw0dAlGda+byYkOHQFTnikrO1dmxb6+YXO2xTUbOq8VI6g+vURMREYkYp76JiEg6TPClHEzUREQkGXqdKK7W1ismaiIiko56XEwmFkzUREQkHZz6JiIiEjETnPrmqm8iIiIRY0VNRETSwWvUREREIsZETQ2lvLwc/16xDt//sAdqzQ042DfHgH4vIeSDITAzM3vgmF9+y8KHYZGV2vd9tw72ds3rLNadew5g8bLVuHL1Gto4t8aEscHo5d8NAFB29y4Wfb4KB9KP4nLuX7CytESPrkpMGBMMRwe7OouJTNeoj4Zi1Kj34dKmNQDgxInTSE5aiB9/2A8A2L5jPZ7v1cNgzIrl6zA+PLreY6VaII6HadYrJmqRWLH2K2z8ZitmRU+Cu2sbHD95CtGz/gUrK0u8//abjxz7/fplsLJsKnxu3uzpasfxy29ZiJ71CX5IW/XA/RnH/kBEbDLGhwQjoGc3bPthH8KnxuOrlYvwbLu2uHOnFH/8eQYhHwyBh3s7FBQWInlBCkIj45D6xcJqx0X0MFeuXMXMmbNxJuc8zMzMMPT9QdiY+jl6+r2GEydOAwBWfrEe8fGfCmNul9xpqHCpplhRU0PJzD6B3s/3QMD/V6atW7XAth/349gffz52bPNmT8PG2uqB+3Q6HVas/Qpff7cdas0NtHFpjTEfDMHLvZ+vVpxrU79Fz+5d8OHQe+9mDRs9HOlHfsOXX29BTEQYrK0ssXxBosGYaRPHYsiof+Kvq3lo1dKxWuclepjt23YbfI6LnYeRo4aiazelkKhLSm4j75r6QcNJakxw1bfRifr69etIS0tDZmam8BJse3t7KJVKDBw4EM2b192U65PMx7MDvv5uO85fvIy2Ls44efosfss6joiwjx479q0PxkFbVgZ317b4x8ih6OzVUdi3bM1GfL9zL2ZOCYOLsxN+zcxG1Mdz0expW3RVehkd5+/HT2DE4CCDNv/uvthzIP0hI4CiohKYmZnB2trS6PMRGUMmk2HgwH6wtGyCXw7/JrQPHvwm3n13AK5dy8e2bbsxO3kRbt9mVS1JvI/60bKysjBq1Cg0btwY/v7+aNu2LQBAo9FgzZo1WLZsGZYvX45OnTrVRaxPtFHD3kFxSQlef280zGUylOt0CB89Aq+9EvjQMQ52zTFzShg6tn8W2rIypG3ZgQ9DI/Hlsvl4zsMdWq0Wy1dvxLIFSfDx7AAAeKZ1K/yWdRxffbu9WolarbkBu+bNDNrsmzeDWnPjgf1LS7X412dfoF+fAFhZMlFT3ejY0QO796ahceOnUFRUgiHvjsHJkzkAgNTU73Dx4hVc/esaOnq2R3xCJBSKdnhvyNgGjpqoaoxK1AkJCejbty/i4uIqLXDS6/WIiYlBQkICNm7cWKtBmoIde/6L73/Yi9mxEXB3bYOTp89i9oIUONo3x5v9XnrgGNc2znBt4yx8VnZ6Dpev/IXVGzcjeeYUXLz8F27fKcVH/5xmMK6s7C46KNyEz137/K9C1pXroC0rM2h77eVAxESEGf2dyu7exaQZidDr9ZgxJdTo8URVderUWfj36A8bW2sMGPAqPv98Hvq+8i5OnszByi/WC/2OH/8T167mYev2L+Hq6oJz5y42YNRULZz6frSTJ08iKSnpgauQzczMMGLECAQFBT1gJD3OJ0tWYNT776BfnxcAAAo3V/x1NQ/L16Q+NFE/iGcHD2RkHQcAlNy+DQD499w4tHCwN+hnYWEh/Jz2nyXCz1nHT+Jfn32BlYvnCG2W9y1Us7drBs11w+pZff0G7O0Mq+yKJJ17LQ9fLExmNU11qqysDGfPXgAAZGZkw9fXC/8YF4zwsOmV+h45kgkAaOfWlolagvRcTPZo9vb2OHbsGNzc3B64/9ixY7C3t3/gPnq0O3dKYSYz/ANIJpNBZ+StCCdPnxVuzXJr6wK53AJ/Xct/5DS3i7OT8PPVPDXMzc0N2u7n3bEDDv2aiWH3XadOP5IB744dhM8VSfripVx8sSgZT9vaGPUdiGpKJpNBLpc/cJ+X13MAgKtX8+ozJKotrKgfbeTIkZgxYways7Ph5+cnJGW1Wo309HR89dVXiIiIqJNAn3Qv9OyOZas2oFULR7i7tsGJUzlYvXETgvq/LPT512crkafWIGnGZADAmo2b0dqpJdxd26BUq0Xadzvwy2+/4/N/JQC4Vwl/MGQQ5iz8HHqdDkqvjigqLkFG1nFYWTY1qlKv8P47byJ4XAT+sz4Nvfy7Yfuu/Th+8jRiI8MB3EvSE6fPwh+ncrBkThx0Oh3UmusAAFsba4NKnqg2xMZNwY8/7MelS1dgbW2Ft995A8/36oE33xgBV1cXvDP4TezcuRfXNTfg2akDkmdH46cDh3E8+2RDh07VwcVkjzZ06FA0a9YM//nPf7B+/XqUl5cDAMzNzdGxY0ckJSWhX79+dRLok27ahLFYtGw1EuYtwfUbN+Fg3xxvv9kPY4PfE/qoNdfx17X/VQFld+9i7qJlyMvXoHHjp6Bwc8Xy+Yno5ust9An7aDiaPW2L5WtScSn3KmysLNHBwx0fDR9crTiVnZ7D7NhILPp8FRak/AdtnFtjYdIMPNuuLQAgL1+DvT8dAnBvNfr9vlg0G906G7+AjehRHBzt8PnyT9CypQMKbhUiO/sk3nxjBPbu+QmtW7dC79498Y9xwbC0bIrLl3Px7Tc7MGf24oYOm6rLBCtqM72+eo95KSsrw40b965VNmvWrMaVUpn6bI3GE0lBM5cXGzoEojpXVHKuzo5dHDuk2mMtY9c/vpMIVfuBJxYWFnB05MMriIiI6hKfTEZERNJhglPfTNRERCQdXExGREQkYqyoiYiIxMsUH3gia+gAiIiIqkynr/5mpGvXrmHy5Mno3r07vLy88Prrr+PYsWPCfr1ejwULFkClUsHLywsffPABzp8/b3CMmzdvYtKkSejcuTO6dOmCadOmobi42Kg4mKiJiIj+5tatWxgyZAgsLCywbNkybN26FZGRkbC1tRX6LFu2DGvWrEFsbCxSU1PRpEkTjBw5EqWlpUKfyZMnIycnBytXrsTSpUtx9OhRzJw506hYOPVNRETSUU/XqJctW4aWLVsiKSlJaHvmmWeEn/V6PVavXo2xY8eiT58+AIA5c+bA398fu3btQv/+/XHmzBkcOHAAX3/9tfBWyejoaIwePRoRERFo0aJFlWJhRU1ERNKh11V702q1KCoqMti0Wu0DT7Nnzx54enoiPDwcfn5+GDBgAFJTU4X9ly9fRn5+Pvz9/YU2a2treHt7IyMjAwCQkZEBGxsbg1c/+/v7QyaTISsrq8pfmYmaiIikowbXqFNSUuDr62uwpaSkPPA0ly5dwvr169G2bVusWLECQ4YMQUJCAjZv3gwAyM/PBwDY2dkZjLOzs4NarQZw7z0YzZs3N9jfqFEj2NraCuOrglPfREQkGfoaTH2HhIQgODjYoO1hb1nT6/Xw9PTExIkTAQDPPfccTp8+jQ0bNtT765xZURMRkXTUoKKWy+WwsrIy2B6WqB0cHCq90rldu3bIzc0V9gOARqMx6KPRaIQ3S9rb2+P69esG++/evYtbt24J46uCiZqIiKRDp6v+ZoTOnTvj3DnDl4ucP38erVu3BgA4OzvDwcEB6enpwv6ioiL8/vvvUCqVAAClUomCggJkZ2cLfQ4dOgSdTgcvr6q/SZCJmoiI6G9GjBiB33//HUuXLsWFCxewZcsWpKam4r337r162MzMDMOHD8dnn32G3bt3488//0RERAQcHR2FVeBubm54/vnnMWPGDGRlZeHXX39FfHw8+vfvX+UV30ANXnNZ2/iaSzIFfM0lmYK6fM1l4T9erfZY639vN6r/3r178emnn+L8+fNwdnZGcHAw3nnnHWG/Xq/HwoULkZqaioKCAvj6+iImJgaurq5Cn5s3byI+Ph579uyBTCbDyy+/jOjoaFhaWlY5DiZqonrERE2moE4T9Zi+1R5rvXRHLUZSf7jqm4iIJEMktWW9YqImIiLp4NuziIiIRMwEEzVXfRMREYkYK2oiIpKMmjyZTKqYqImISDqYqImIiETMuAeMPRGYqImISDI49U1ERCRmTNREREQiZoJT37w9i4iISMRYURMRkWTwGjUREZGYmeDUNxM1ERFJBitqIiIiMWNFTUREJF56E0zUXPVNREQkYqyoiYhIOkywomaiJiIiyTDFqW8maiIikg4maiIiIvFiRU1ERCRiTNREREQiZoqJmrdnERERiRgraiIikg69WUNHUO9YURMRkWToddXfjLFo0SJ4eHgYbH379hX2l5aWIi4uDt27d4dSqURYWBjUarXBMXJzczF69Gh4e3vDz88Ps2fPxt27d43+zqyoiYhIMvS6+quon332WaxcuVL4bG5uLvycmJiI/fv3Y/78+bC2tkZ8fDxCQ0OxYcMGAEB5eTlCQkJgb2+PDRs2IC8vD5GRkbCwsMDEiRONioMVNRERSUZ9VdTAvcTs4OAgbM2bNwcAFBYWIi0tDVFRUfDz84OnpycSExORkZGBzMxMAMBPP/2EnJwczJ07Fx06dEBAQADGjx+PdevWQavVGhUHEzUREUmGXm9W7U2r1aKoqMhge1TSvHDhAlQqFV588UVMmjQJubm5AIDs7GyUlZXB399f6Ovm5gYnJychUWdmZkKhUMDe3l7oo1KpUFRUhJycHKO+M6e+iYjIJKSkpGDx4sUGbaGhoQgLC6vU18vLC0lJSXB1dUV+fj6WLFmCoUOHYsuWLVCr1bCwsICNjY3BGDs7O+Tn5wMA1Gq1QZIGIHyu6FNVTNRERCQZNbmPOiQkBMHBwQZtcrn8gX0DAgKEn9u3bw9vb2/07t0b27dvR+PGjasfRDVw6puIiCRDrzOr9iaXy2FlZWWwPSxR/52NjQ3atm2Lixcvwt7eHmVlZSgoKDDoo9Fo4ODgAOBe9fz3VeAVnyv6VBUTNRERSYZeX/2tJoqLi3Hp0iU4ODjA09MTFhYWSE9PF/afPXsWubm58PHxAQD4+Pjg1KlT0Gg0Qp+DBw/CysoK7u7uRp2bU99ERCQZ9XV71uzZs9G7d284OTkhLy8PixYtgkwmw2uvvQZra2sMGjQIycnJsLW1hZWVFRISEqBUKoVErVKp4O7ujoiICEyZMgX5+fmYP38+hg4dWuUqvgITNRERSUZ9JeqrV69i4sSJuHnzJpo3bw5fX1+kpqYKt2hNmzYNMpkM4eHh0Gq1UKlUiImJEcabm5tj6dKliI2NxeDBg9GkSRMEBQUhPDzc6FjM9PqaTgjUjjL12YYOgajONXN5saFDIKpzRSXn6uzY57xfqvZY199/rMVI6g+vURMREYkYp76JiEgy6vMRomLBRE1ERJKhN8G3ZzFRExGRZNTkgSdSxURNRESSoWNFTUREJF6c+iYiIhIxU1xMxtuziIiIRIwVNRERSYY4HtFVv5ioiYhIMkxx6puJmoiIJIOrvomIiESMq76JiIhEzBSvUXPVNxERkYixoiYiIsngNWoiIiIR4zVqIiIiETPFa9RM1EREJBmc+m5ATZyeb+gQiOpcVwdFQ4dAJGmc+iYiIhIxU6yoeXsWERGRiLGiJiIiyTDBtWRM1EREJB2mOPXNRE1ERJLBxWREREQipmvoABoAF5MREZFk6GFW7a26Pv/8c3h4eGDWrFlCW2lpKeLi4tC9e3colUqEhYVBrVYbjMvNzcXo0aPh7e0NPz8/zJ49G3fv3jX6/EzURERED5GVlYUNGzbAw8PDoD0xMRF79+7F/PnzsWbNGuTl5SE0NFTYX15ejpCQEJSVlWHDhg1ITk7G5s2bsXDhQqNjYKImIiLJ0OmrvxmruLgYU6ZMQUJCAmxtbYX2wsJCpKWlISoqCn5+fvD09ERiYiIyMjKQmZkJAPjpp5+Qk5ODuXPnokOHDggICMD48eOxbt06aLVao+JgoiYiIsnQwazam1arRVFRkcH2qKT58ccfIyAgAP7+/gbt2dnZKCsrM2h3c3ODk5OTkKgzMzOhUChgb28v9FGpVCgqKkJOTo5R35mLyYiISDJqcq05JSUFixcvNmgLDQ1FWFhYpb5bt27FH3/8ga+//rrSPrVaDQsLC9jY2Bi029nZIT8/X+hzf5IGIHyu6FNVTNRERCQZNVn1HRISguDgYIM2uVxeqd9ff/2FWbNm4YsvvsBTTz1VgzPWDiZqIiKSjJpU1HK5/IGJ+e+OHz8OjUaDgQMHCm3l5eU4cuQI1q1bhxUrVqCsrAwFBQUGVbVGo4GDgwOAe9VzVlaWwXErVoVX9KkqJmoiIpKM+riPukePHtiyZYtB29SpU9GuXTt89NFHaNWqFSwsLJCeno5XXnkFAHD27Fnk5ubCx8cHAODj44OlS5dCo9HAzs4OAHDw4EFYWVnB3d3dqHiYqImIiO5jZWUFhcLwlbRNmzbF008/LbQPGjQIycnJsLW1hZWVFRISEqBUKoVErVKp4O7ujoiICEyZMgX5+fmYP38+hg4dWqWq/n5M1EREJBlieTLZtGnTIJPJEB4eDq1WC5VKhZiYGGG/ubk5li5ditjYWAwePBhNmjRBUFAQwsPDjT6XmV6vF8XLSBrJWzd0CER1rquD4vGdiCQu/creOjv21hZDqj22/7X1tRhJ/WFFTUREkqEzvXdyMFETEZF06Gqw6luqmKiJiEgyRHGttp7xEaJEREQixoqaiIgkQyyrvusTEzUREUmGzozXqImIiETLFK9RM1ETEZFkcOqbiIhIxHgfNRERkYiZ4n3UvD2LiIhIxFhRExGRZHAxGRERkYjxGjUREZGIcdU3ERGRiHHqm4iISMQ49U1ERCRipjj1zduziIiIRIwVNRERSYYpVtRM1EREJBl6XqMmIiISL1bUREREIsZETUREJGKmeB81V30TERGJGCtqIiKSDFN84AkraiIikgxdDTZjfPnll3j99dfRuXNndO7cGYMHD8b+/fuF/aWlpYiLi0P37t2hVCoRFhYGtVptcIzc3FyMHj0a3t7e8PPzw+zZs3H37l2jvzMTNRERSUZ9JeqWLVti8uTJ2LRpE9LS0tCjRw+MGzcOp0+fBgAkJiZi7969mD9/PtasWYO8vDyEhoYK48vLyxESEoKysjJs2LABycnJ2Lx5MxYuXGj0d2aiJiIiydDXYDNGYGAgAgIC0LZtW7i6umLChAlo2rQpMjMzUVhYiLS0NERFRcHPzw+enp5ITExERkYGMjMzAQA//fQTcnJyMHfuXHTo0AEBAQEYP3481q1bB61Wa1QsTNRERCQZOrPqb1qtFkVFRQZbVZJmeXk5tm7dipKSEiiVSmRnZ6OsrAz+/v5CHzc3Nzg5OQmJOjMzEwqFAvb29kIflUqFoqIi5OTkGPWduZiMiIgkoyb3UaekpGDx4sUGbaGhoQgLC3tg/z///BPvvvsuSktL0bRpUyxZsgTu7u44ceIELCwsYGNjY9Dfzs4O+fn5AAC1Wm2QpAEInyv6VBUTNRERmYSQkBAEBwcbtMnl8of2d3V1xTfffIPCwkLs3LkTkZGRWLt2bV2HWQkTNRERSUZNHngil8sfmZgf1L9NmzYAAE9PTxw7dgyrV6/Gq6++irKyMhQUFBhU1RqNBg4ODgDuVc9ZWVkGx6tYFV7Rp6p4jZqIiCRDB321txqfW6eDVquFp6cnLCwskJ6eLuw7e/YscnNz4ePjAwDw8fHBqVOnoNFohD4HDx6ElZUV3N3djTovK2oiIpKM+nrW9yeffIJevXqhVatWKC4uxvfff49ffvkFK1asgLW1NQYNGoTk5GTY2trCysoKCQkJUCqVQqJWqVRwd3dHREQEpkyZgvz8fMyfPx9Dhw41qqoHmKiJiEhC6utZ3xqNBpGRkcjLy4O1tTU8PDywYsUK9OzZEwAwbdo0yGQyhIeHQ6vVQqVSISYmRhhvbm6OpUuXIjY2FoMHD0aTJk0QFBSE8PBwo2Mx0+v1onjGeSN564YOgajOdXVQNHQIRHUu/creOjt2bJuh1R97YV0tRlJ/eI2aiIhIxDj1TUREkmGKL+VgoiYiIsmojdXbUsNETUREkmF6aZqJmoiIJKS+bs8SEyZqIiKSDE59ExERiZjppWnenkVERCRqrKiJiEgyeI2aiIhIxHiNmoiISMRML00zURMRkYRw6puIiEjE9CZYU3PVNxERkYixoiYiIsng1DcREZGImeKqb059P8GeV3XHN5v/g4vnf8Vd7RW88cYrBvsHDHgV27d+iWt/ZeOu9gq8vTs2UKRkKny6e2Huf2bhu1+/QvqVvej1Ss8qj/Xq4okDF3Zh1Q/L6jDCewJfC8CG/auw78xOrN21An6B3YV95o3M8Y9po7F21wrsOb0N3/36FWYumAr7FnZ1HhfdW/Vd3U2qmKifYJaWTZGV9QfCxk9/6P6fD/6CqdNm1XNkZKoaN22M03+cwSfTFxg1zsrGEjMWROHoT7/VOAalnzc2HVr/0P2dunRE3JIZ2LJ+G0a88hH+u/MnzF4Rj3YebQEAjZs0hkenZ7FywRp80DcEUz+aCZd2z2DOSv53VB900Fd7kypOfT/Bduzcix079z50/7p1aQCANm2c6yskMnGH9v6CQ3t/MXpcRPJE/PjNbpSX69Crr8pgn5mZGYaNG4I3h74GO4fmuHjuMlbOX429W/9brRjfGTkIh/f9gnVLNwIAPp+7El17dcFbwUGYE/UvFBcWY/yQKQZjPolegC+2LUULJ0dcy82r1nmpakzxGjUraiIStf7v9EVrl1ZY8emqB+4fHvYeXn3rZcyJ+hfeCwzGhmVfIWbhdCh7eFfrfJ6+z+HIgV8N2g7vOwJP34dfGrKysYROp0NhQVG1zklVp6/BP1JV64n6r7/+wtSpU2v7sERkgpxdW+Mf0z5CbHgiyssr11IWcguMCBuKWZPm4PD+I8i9+Be2pe7Ezk0/YsD7r1frnHYOzXE9/4ZB23X1Ddg5NHtgf/lTFvjHtBD8+M0elBSVVOucRI9S61Pft27dwjfffIOkpKTaPjQRmRCZTIa4xdFY/sl/cOns5Qf2cW7bGk2aNsGC9fMM2i0sGuFUdo7wefepbcLP5jIZLJ6yMGjbuelHzIn6l9ExmjcyR8LSGJiZAXOmGj+ejGeKU99GJ+rdu3c/cv+lS5eqHQwRUYWmVk3wnE97KDyfxcSE8QAAmcwMMpkMBy7swj/fm4LbJXcAAJOHT0X+1XyD8VptmfDziJdHCT8/p3wO46aPxri3/im0FRf+rxLW5F9H879Vz83tm0HztyrbvJE5Zi2NQUvnlgh9ZyKr6Xoi5Sns6jI6UY8bNw5mZmbQ6x/+yzIzM6tRUERExYUlGBoYbNA2cMQAdOmpxLTRMci9eBUymRlK72jRorUjMg79/tBjXT6fK/zs0MoB5XfLDdrul/3rH+ii6oyNy9OEtm69fJH963Hhc0WSdnZ1RujbE1Bwo6C6X5OMxIq6ChwcHBATE4M+ffo8cP+JEycwcODAGgdGNWdp2RTu7q7CZ9e2LvD27ojr12/g0qVcNGv2NFxcWsOpVQsAgELhBgC4ejUP167lP/CYRDXRpGljOLu2Fj47ubTCsx3dUHCjENdy8zA2ahQcWjng4/FJ0Ov1OPvneYPxN9Q3UFqqNWj/MmUjxseOg0wmw++/HIOltSW8unqipKgE277aaXSMqSvS8O+v52NIyNs4uOsQ+rwZiPZeHkiO+ATAvSSd+HkcPDo9i8kjpkFmLhMq8IKbhbhbdtf4XwxVme4RReKTyuhE3bFjRxw/fvyhifpx1TbVny6+3ti962vh8yfzYgEAq1anYuSoCXj9tZfxxYr/XVdbv+4zAMDH8Z/g4/hP6zVWMg3tvT3w76/nC5/Hx44DAGxN3YGECbNh18IOLZwcjTrm53O+wE3NLQwPfQ9OLq1QWFCEU8dOY9WiddWK8djR44gJTcDoiA8xJnIULp27gsiRM4Q/Dhxa2gsPalnz43KDsf9465/ISH94ZU81V1/ZJSUlBT/88APOnj2Lxo0bQ6lUYvLkyWjXrp3Qp7S0FMnJydi2bRu0Wi1UKhViYmJgb28v9MnNzUVsbCwOHz6Mpk2bYsCAAZg0aRIaNap6+jXTG5lVjx49ipKSEvTq1euB+0tKSpCdnY1u3boZc1g0krd+fCciievqoGjoEIjqXPqVhz+/oabeaxNU7bFfXthc5b4jR45E//790alTJ5SXl+PTTz/F6dOnsXXrVjRt2hQAEBMTg/379yMpKQnW1taIj4+HmZkZNmzYAAAoLy/HgAEDYG9vj4iICOTl5SEyMhLvvPMOJk6cWOVYjE7UdYWJmkwBEzWZgichUf/d9evX4efnh7Vr16Jr164oLCyEn58f5s2bh759+wIAzpw5g379+mHjxo3w8fHB/v37MWbMGBw4cECostevX4958+YhPT0dcrm8SufmA0+IiEgyavLAE61Wi6KiIoNNq9VW6byFhYUAAFtbWwBAdnY2ysrK4O/vL/Rxc3ODk5MTMjMzAQCZmZlQKBQGU+EqlQpFRUXIyfnf7YOPw0RNRESSoavBlpKSAl9fX4MtJSXl8efU6ZCYmIjOnTtDobg3K6ZWq2FhYQEbGxuDvnZ2dsjPzxf63J+kAQifK/pUBZ/1TUREklGTl2uEhIQgONjwlr+qTD/HxcXh9OnT+PLLL6t97ppgoiYiIsmoyQNP5HJ5la8LV/j444+xb98+rF27Fi1bthTa7e3tUVZWhoKCAoOqWqPRwMHBQeiTlZVlcDy1Wg0AQp+q4NQ3ERFJRk2mvo2h1+vx8ccf48cff8SqVavwzDPPGOz39PSEhYUF0tPThbazZ88iNzcXPj4+AAAfHx+cOnUKGo1G6HPw4EFYWVnB3d29yrGwoiYiIvqbuLg4fP/99/j3v/8NS0tL4ZqytbU1GjduDGtrawwaNAjJycmwtbWFlZUVEhISoFQqhUStUqng7u6OiIgITJkyBfn5+Zg/fz6GDh1qVGXP27OI6hFvzyJTUJe3ZwW5VO+taACw+eKWKvf18PB4YHtSUpLw9M2KB55s3brV4IEn909rX7lyBbGxsfjll1/QpEkTBAUF1f0DT+oKEzWZAiZqMgV1majfdHmt2mO/vfh9LUZSfzj1TUREksGXchAREYkYX3NJREQkYjW5j1qqmKiJiEgyRLKsql7xPmoiIiIRY0VNRESSwcVkREREIsbFZERERCLGxWREREQiZoqLyZioiYhIMkyxouaqbyIiIhFjRU1ERJLBxWREREQipuM1aiIiIvEyvTTNRE1ERBJiiovJmKiJiEgymKiJiIhEzBTvo+btWURERCLGipqIiCSDU99EREQixvuoiYiIRMwUr1EzURMRkWRw6puIiEjETLGi5qpvIiIiEWNFTUREkmGKU9+sqImISDL0NfjHGEeOHMGYMWOgUqng4eGBXbt2Gcah12PBggVQqVTw8vLCBx98gPPnzxv0uXnzJiZNmoTOnTujS5cumDZtGoqLi43+zkzUREQkGTq9vtqbMUpKSuDh4YGYmJgH7l+2bBnWrFmD2NhYpKamokmTJhg5ciRKS0uFPpMnT0ZOTg5WrlyJpUuX4ujRo5g5c6bR35lT30REJBn1dR91QEAAAgICHhyDXo/Vq1dj7Nix6NOnDwBgzpw58Pf3x65du9C/f3+cOXMGBw4cwNdff41OnToBAKKjozF69GhERESgRYsWVY6FFTUREUlGTSpqrVaLoqIig02r1Rodw+XLl5Gfnw9/f3+hzdraGt7e3sjIyAAAZGRkwMbGRkjSAODv7w+ZTIasrCyjzsdETUREklGTa9QpKSnw9fU12FJSUoyOIT8/HwBgZ2dn0G5nZwe1Wg0AUKvVaN68ucH+Ro0awdbWVhhfVZz6JiIikxASEoLg4GCDNrlc3kDRVB0TNRERSYaxi8LuJ5fLayUxOzg4AAA0Gg0cHR2Fdo1Gg/bt2wMA7O3tcf36dYNxd+/exa1bt4TxVcWpbyIikoz6uj3rUZydneHg4ID09HShraioCL///juUSiUAQKlUoqCgANnZ2UKfQ4cOQafTwcvLy6jzsaImIiLJqElFbYzi4mJcvHhR+Hz58mWcOHECtra2cHJywvDhw/HZZ5+hTZs2cHZ2xoIFC+Do6CisAndzc8Pzzz+PGTNmIC4uDmVlZYiPj0f//v2NWvENAGZ6kTw4tZG8dUOHQFTnujooGjoEojqXfmVvnR27nb2y2mPPqjOq3Pfw4cMYPnx4pfagoCAkJydDr9dj4cKFSE1NRUFBAXx9fRETEwNXV1eh782bNxEfH489e/ZAJpPh5ZdfRnR0NCwtLY2Km4maqB4xUZMpqMtE7WrnXe2x5zS/12Ik9YfXqImIiESM16iJiEgyTPGlHEzUREQkGSK5WluvmKiJiEgyWFETERGJGCtqIiIiEauv+6jFhImaiIgko75ecykmvD2LiIhIxFhRExGRZPAaNRERkYhx1TcREZGIsaImIiISMa76JiIiEjFW1ERERCJmiteoeXsWERGRiLGiJiIiyeDUNxERkYhxMRkREZGImeIjRJmoiYhIMlhRExERiZgpXqPmqm8iIiIRY0VNRESSwWvUREREImaKU99M1EREJBlM1ERERCJmemkaMNOb4p8nREREEsFV30RERCLGRE1ERCRiTNREREQixkRNREQkYkzUREREIsZETUREJGJM1ERERCLGRE1ERCRiTNREREQixkRNREQkYkzUJmjdunUIDAxEp06d8PbbbyMrK6uhQyKqVUeOHMGYMWOgUqng4eGBXbt2NXRIRNXGRG1itm3bhqSkJIwbNw6bN29G+/btMXLkSGg0moYOjajWlJSUwMPDAzExMQ0dClGN8aUcJubtt99Gp06dMHPmTACATqdDQEAAhg0bhtGjRzdwdES1z8PDA0uWLEGfPn0aOhSiamFFbUK0Wi2OHz8Of39/oU0mk8Hf3x8ZGRkNGBkRET0ME7UJuXHjBsrLy2FnZ2fQbmdnB7Va3UBRERHRozBRExERiRgTtQlp1qwZzM3NKy0c02g0sLe3b6CoiIjoUZioTYhcLkfHjh2Rnp4utOl0OqSnp0OpVDZgZERE9DCNGjoAql/BwcGIjIyEp6cnvLy8sGrVKty+fRsDBw5s6NCIak1xcTEuXrwofL58+TJOnDgBW1tbODk5NWBkRMbj7VkmaO3atVixYgXy8/PRoUMHREdHw9vbu6HDIqo1hw8fxvDhwyu1BwUFITk5uQEiIqo+JmoiIiIR4zVqIiIiEWOiJiIiEjEmaiIiIhFjoiYiIhIxJmoiIiIRY6ImIiISMSZqIiIiEWOiJiIiEjEmaiIiIhFjoiYiIhIxJmoiIiIRY6ImIiISsf8D1TmU1T00kMoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Model Evaluation | Confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(dpi=100)\n",
        "sns.heatmap(cm, annot=True)\n",
        "plt.title(\"Confusion matrix\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}