Spam_Mail_Prediction_using_Machine_Learning.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/divyadevchoudhary/Banking-System-Basic/blob/main/Spam_Mail_Prediction_using_Machine_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqB21QOgMg-G"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rALI06-oHusw"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyKe9o2ONeFv"
      },
      "source": [
        "Data Collection & Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpStHH8KNcYB"
      },
      "source": [
        "# loading the data from csv file to a pandas Dataframe\n",
        "raw_mail_data = pd.read_csv('/content/mail_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdn-7VE2NxsZ",
        "outputId": "28c19d96-23a2-43c0-86ad-5c1aee7f1b58"
      },
      "source": [
        "print(raw_mail_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Category                                            Message\n",
            "0         ham  Go until jurong point, crazy.. Available only ...\n",
            "1         ham                      Ok lar... Joking wif u oni...\n",
            "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
            "3         ham  U dun say so early hor... U c already then say...\n",
            "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
            "...       ...                                                ...\n",
            "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
            "5568      ham               Will ü b going to esplanade fr home?\n",
            "5569      ham  Pity, * was in mood for that. So...any other s...\n",
            "5570      ham  The guy did some bitching but I acted like i'd...\n",
            "5571      ham                         Rofl. Its true to its name\n",
            "\n",
            "[5572 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhakjIE1N011"
      },
      "source": [
        "# replace the null values with a null string\n",
        "mail_data = raw_mail_data.where((pd.notnull(raw_mail_data)),'')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "SJey6H-SOWeK",
        "outputId": "af1b0dfd-2ff9-4af9-cfcd-d0c177dd6ab9"
      },
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "mail_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Category                                            Message\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbK82N2gOdar",
        "outputId": "4d1840a1-22b5-468f-d4d0-a4528ef4313c"
      },
      "source": [
        "# checking the number of rows and columns in the dataframe\n",
        "mail_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhR4U3ATPBdk"
      },
      "source": [
        "Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EW7QSgeOt4p"
      },
      "source": [
        "# label spam mail as 0;  ham mail as 1;\n",
        "\n",
        "mail_data.loc[mail_data['Category'] == 'spam', 'Category',] = 0\n",
        "mail_data.loc[mail_data['Category'] == 'ham', 'Category',] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxZK1fWwPwII"
      },
      "source": [
        "spam  -  0\n",
        "\n",
        "ham  -  1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8Rt-FaNPtPE"
      },
      "source": [
        "# separating the data as texts and label\n",
        "\n",
        "X = mail_data['Message']\n",
        "\n",
        "Y = mail_data['Category']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QnQeUBGtQPP7",
        "outputId": "a2640f4b-2a1d-4742-9742-3ecbb6017668"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       Go until jurong point, crazy.. Available only ...\n",
            "1                           Ok lar... Joking wif u oni...\n",
            "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
            "3       U dun say so early hor... U c already then say...\n",
            "4       Nah I don't think he goes to usf, he lives aro...\n",
            "                              ...                        \n",
            "5567    This is the 2nd time we have tried 2 contact u...\n",
            "5568                 Will ü b going to esplanade fr home?\n",
            "5569    Pity, * was in mood for that. So...any other s...\n",
            "5570    The guy did some bitching but I acted like i'd...\n",
            "5571                           Rofl. Its true to its name\n",
            "Name: Message, Length: 5572, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuWDNy5KQQjY",
        "outputId": "1a0a109b-d63a-4cf0-fe4e-b486f1d3d623"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       1\n",
            "1       1\n",
            "2       0\n",
            "3       1\n",
            "4       1\n",
            "       ..\n",
            "5567    0\n",
            "5568    1\n",
            "5569    1\n",
            "5570    1\n",
            "5571    1\n",
            "Name: Category, Length: 5572, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvHyqdH8QZPH"
      },
      "source": [
        "Splitting the data into training data & test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RO2GmbSNQSQH"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tS2c7A4NRa46",
        "outputId": "5d44247f-65d0-457d-8a94-0fd8b45a3b72"
      },
      "source": [
        "print(X.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5572,)\n",
            "(4457,)\n",
            "(1115,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYQpiACGSBYM"
      },
      "source": [
        "Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLs847nSRibm"
      },
      "source": [
        "# transform the text data to feature vectors that can be used as input to the Logistic regression\n",
        "\n",
        "feature_extraction = TfidfVectorizer(min_df = 1, stop_words='english', lowercase='True')\n",
        "\n",
        "X_train_features = feature_extraction.fit_transform(X_train)\n",
        "X_test_features = feature_extraction.transform(X_test)\n",
        "\n",
        "# convert Y_train and Y_test values as integers\n",
        "\n",
        "Y_train = Y_train.astype('int')\n",
        "Y_test = Y_test.astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBMAcw9RUkUY"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NFuGogZUpt0"
      },
      "source": [
        "print(X_train_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q86FvELbU_SV"
      },
      "source": [
        "Training the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hV6BAIZQVBbo"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JeAOwzpUv0V"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWGRHWAPVI_z",
        "outputId": "1c5e15dd-0e07-4871-c4fa-b908ee400b55"
      },
      "source": [
        "# training the Logistic Regression model with the training data\n",
        "model.fit(X_train_features, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZ01fa8dVeL5"
      },
      "source": [
        "Evaluating the trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExiF2kKxVYtC"
      },
      "source": [
        "# prediction on training data\n",
        "\n",
        "prediction_on_training_data = model.predict(X_train_features)\n",
        "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7t4DI5UWCkB",
        "outputId": "49fafbb0-0e7f-40c7-9ab7-4aea165731ee"
      },
      "source": [
        "print('Accuracy on training data : ', accuracy_on_training_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data :  0.9670181736594121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTin5rXTWKg3"
      },
      "source": [
        "# prediction on test data\n",
        "\n",
        "prediction_on_test_data = model.predict(X_test_features)\n",
        "accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gvoMK4OWnJY",
        "outputId": "7bf56da4-1987-4828-ea00-95c30fb083d1"
      },
      "source": [
        "print('Accuracy on test data : ', accuracy_on_test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data :  0.9659192825112107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXdOKxYAXaHC"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h60z1__mWql6",
        "outputId": "3aac53f3-13f2-4afb-e9f2-75d337cbcd44"
      },
      "source": [
        "input_mail = [\"I've been searching for the right words to thank you for this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times\"]\n",
        "\n",
        "# convert text to feature vectors\n",
        "input_data_features = feature_extraction.transform(input_mail)\n",
        "\n",
        "# making prediction\n",
        "\n",
        "prediction = model.predict(input_data_features)\n",
        "print(prediction)\n",
        "\n",
        "\n",
        "if (prediction[0]==1):\n",
        "  print('Ham mail')\n",
        "\n",
        "else:\n",
        "  print('Spam mail')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "Ham mail\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_LqbM_ZYwS1"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}