model.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "#### Read and Clean Text:"
      ],
      "metadata": {
        "id": "mWI1AbmcQXad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
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        },
        "id": "mZGB0NIkRN_W",
        "outputId": "b070aecc-38c6-4fd2-d394-1eb71869c43c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
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              "True"
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          "execution_count": 4
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "stopwords = nltk.corpus.stopwords.words('english')\n",
        "pd.set_option('display.max_colwidth',100)\n",
        "\n",
        "messages = pd.read_csv('/content/spam.csv', encoding='latin-1')\n",
        "messages = messages.drop(labels=[\"Unnamed: 2\",\"Unnamed: 3\", \"Unnamed: 4\"], axis = 1)\n",
        "messages.columns = [\"label\", \"text\"]\n",
        "\n",
        "def clean_text(text):\n",
        "  text = \"\".join([word.lower() for word in text if word not in string.punctuation])\n",
        "  tokens = re.split('\\W+', text)\n",
        "  text = [word for word in tokens if word not in stopwords]\n",
        "  return text\n",
        "\n",
        "tfidf_vect = TfidfVectorizer(analyzer=clean_text)\n",
        "x_tfidf = tfidf_vect.fit_transform(messages['text'])\n",
        "\n",
        "X_features = pd.DataFrame(x_tfidf.toarray())\n",
        "X_features.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model: Random Forest Classifieer"
      ],
      "metadata": {
        "id": "ALaA_N-eRmCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RandomForestClassifier().get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NotIK4MYRHJL",
        "outputId": "a4a32c13-7414-48e6-92b5-730b7e036c95"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': 'auto',\n",
              " 'max_leaf_nodes': None,\n",
              " 'max_samples': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': None,\n",
              " 'oob_score': False,\n",
              " 'random_state': None,\n",
              " 'verbose': 0,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split train-test dataset."
      ],
      "metadata": {
        "id": "gSBbGZ61TxG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "5N8VYGZARzpM"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_features,\n",
        "                                                    messages['label'],\n",
        "                                                    test_size=0.2)"
      ],
      "metadata": {
        "id": "ch1dwWyZTpvu"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting a model:"
      ],
      "metadata": {
        "id": "I4ylcoIvT2um"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier()\n",
        "rf_model = rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "5IYHE7NtTrEk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make Predictioon:"
      ],
      "metadata": {
        "id": "hpWz3nZwUUz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_model.predict(X_test)"
      ],
      "metadata": {
        "id": "YkZQMTEYUTY0"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate Model Prediction:"
      ],
      "metadata": {
        "id": "Ekw_Zv-TUn2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred, pos_label='spam')\n",
        "recall = recall_score(y_test, y_pred, pos_label='spam')\n",
        "print('Precision: {}/ Recall:{}'.format(round(precision,3), round(recall,3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuZBDEf-UmFt",
        "outputId": "91678b8a-5244-4f77-f760-565603db0b29"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 1.0/ Recall:0.868\n"
          ]
        }
      ]
    }
  ]
}