ENEL645_Project.ipynb

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      "cell_type": "markdown",
      "source": [
        "Installations and imports"
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            "Requirement already satisfied: yake in /usr/local/lib/python3.7/dist-packages (0.4.8)\n",
            "Requirement already satisfied: segtok in /usr/local/lib/python3.7/dist-packages (from yake) (1.5.11)\n",
            "Requirement already satisfied: jellyfish in /usr/local/lib/python3.7/dist-packages (from yake) (0.9.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from yake) (0.8.9)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from yake) (2.6.3)\n",
            "Requirement already satisfied: click>=6.0 in /usr/local/lib/python3.7/dist-packages (from yake) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from yake) (1.21.5)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from segtok->yake) (2019.12.20)\n",
            "Requirement already satisfied: stop-words in /usr/local/lib/python3.7/dist-packages (2018.7.23)\n"
          ]
        }
      ],
      "source": [
        "!pip install yake\n",
        "!pip install stop-words\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "from sklearn import feature_extraction, linear_model, model_selection, preprocessing\n",
        "import yake\n",
        "from operator import itemgetter\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from stop_words import get_stop_words\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem.porter import PorterStemmer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading the data into training and testing dataframes**"
      ],
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        "id": "k55eAs_tn5J2"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kBeTRWGCM4KF"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv(\"/content/ENEL645/train.csv\")\n",
        "test_df = pd.read_csv(\"/content/ENEL645/test.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset exploration**"
      ],
      "metadata": {
        "id": "UAF-vgFIoEpe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(train_df)"
      ],
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              "         id keyword location  \\\n",
              "0         1     NaN      NaN   \n",
              "1         4     NaN      NaN   \n",
              "2         5     NaN      NaN   \n",
              "3         6     NaN      NaN   \n",
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              "7612  10873     NaN      NaN   \n",
              "\n",
              "                                                   text  target  \n",
              "0     Our Deeds are the Reason of this #earthquake M...       1  \n",
              "1                Forest fire near La Ronge Sask. Canada       1  \n",
              "2     All residents asked to 'shelter in place' are ...       1  \n",
              "3     13,000 people receive #wildfires evacuation or...       1  \n",
              "4     Just got sent this photo from Ruby #Alaska as ...       1  \n",
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              "7608  Two giant cranes holding a bridge collapse int...       1  \n",
              "7609  @aria_ahrary @TheTawniest The out of control w...       1  \n",
              "7610  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1  \n",
              "7611  Police investigating after an e-bike collided ...       1  \n",
              "7612  The Latest: More Homes Razed by Northern Calif...       1  \n",
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      "cell_type": "code",
      "source": [
        "#Checking to see how many NaN values are in the dataset\n",
        "\n",
        "train_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pO-_8TmmoJF",
        "outputId": "54e10347-7d21-4815-ac34-40ee94ddb565"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id             0\n",
              "keyword       61\n",
              "location    2533\n",
              "text           0\n",
              "target         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "source": [
        "#Checking for an imbalanced dataset\n",
        "\n",
        "train_df.target.value_counts()"
      ],
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        "colab": {
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            "text/plain": [
              "0    4342\n",
              "1    3271\n",
              "Name: target, dtype: int64"
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          "metadata": {},
          "execution_count": 5
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    {
      "cell_type": "markdown",
      "source": [
        "**Extracting keywords to fill NaN values**"
      ],
      "metadata": {
        "id": "veVvpOGto-ve"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G27nP7a0NhbE"
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      "outputs": [],
      "source": [
        "#Extracting keywords for the training set and test sets\n",
        "\n",
        "extractor = yake.KeywordExtractor(n=2)\n",
        "\n",
        "for index, row in train_df.iterrows():\n",
        "  if pd.isna(row['keyword']):\n",
        "    keywords = extractor.extract_keywords(row[\"text\"])\n",
        "    row[\"keyword\"] = max(keywords, key=itemgetter(1))[0]\n",
        "    train_df.at[index, \"keyword\"] = row[\"keyword\"]\n",
        "\n",
        "for index, row in test_df.iterrows():\n",
        "  if pd.isna(row['keyword']):\n",
        "    keywords = extractor.extract_keywords(row[\"text\"])\n",
        "    if not keywords:\n",
        "      row[\"keyword\"] = \"null\"\n",
        "    else:\n",
        "      row[\"keyword\"] = max(keywords, key=itemgetter(1))[0]\n",
        "    test_df.at[index, \"keyword\"] = row[\"keyword\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data preprocessing**"
      ],
      "metadata": {
        "id": "pm8iGJWGpK2m"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Em63p9i_QV94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c98ced3d-5fc7-40e5-c784-eed9449a93f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "#Tokenization\n",
        "\n",
        "nltk.download('punkt')\n",
        "train_tokens = train_df.apply(lambda row: nltk.word_tokenize(row['text']), axis=1)\n",
        "test_tokens = test_df.apply(lambda row: nltk.word_tokenize(row['text']), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CgMI-IEyeCpG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00961373-5e5c-4039-b322-23198a97bfc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "#Stop word removal. Added additional cases from manual examination of the dataset\n",
        "\n",
        "nltk.download('stopwords')\n",
        "\n",
        "eng_stopwords = stopwords.words('english') \n",
        "        \n",
        "stop_en = list(get_stop_words('en'))\n",
        "stop_en.extend(list(stopwords.words('english')) )\n",
        "stop_en.extend(['.',',','', 'i i','?','\\'\\'',\"''\",'y','S','@','*','out','==','df','e.g.','\\'m','\\[',\"'m\",':', ')', '(','n\\'t', '\\'','``','``','\\'s', \n",
        "                'https://','-', \"#\", \"^\", \"§\", \"║\", \"«\", \"╔\", \"!\", \"...\", \"//t\", \"//\", \"http\", \"Ã\", \"·\", \"´\", \"[\", \"]\", \"=\", \"&\", \">\", \"<\", \"http://t.co/\", \"//t.co\",\"Â‰Ã\"])\n",
        "train_stopwords=train_tokens.apply(lambda words: [word for word in words if word not in stop_en])\n",
        "test_stopwords=test_tokens.apply(lambda words: [word for word in words if word not in stop_en])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zXilw2AKiyeS"
      },
      "outputs": [],
      "source": [
        "#Stemming\n",
        "\n",
        "ps=PorterStemmer()\n",
        "train_stemming=train_stopwords.apply(lambda x: [ps.stem(word) for word in x])\n",
        "test_stemming=test_stopwords.apply(lambda x: [ps.stem(word) for word in x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKTh_oIsjmKS"
      },
      "outputs": [],
      "source": [
        "#Saving the preprocessed text back to the dataframe\n",
        "\n",
        "train_df['text'] = train_stemming\n",
        "test_df['text'] = test_stemming"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
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              "   id     keyword location                                               text  \\\n",
              "0   1  earthquake      NaN  [our, deed, reason, earthquak, may, allah, for...   \n",
              "1   4        fire      NaN       [forest, fire, near, La, rong, sask, canada]   \n",
              "2   5  evacuation      NaN  [all, resid, ask, 'shelter, place, notifi, off...   \n",
              "3   6  evacuation      NaN  [13,000, peopl, receiv, wildfir, evacu, order,...   \n",
              "4   7       photo      NaN  [just, got, sent, photo, rubi, alaska, smoke, ...   \n",
              "\n",
              "   target  \n",
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              "        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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>location</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[our, deed, reason, earthquak, may, allah, for...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>fire</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[forest, fire, near, La, rong, sask, canada]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>evacuation</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[all, resid, ask, 'shelter, place, notifi, off...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>evacuation</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[13,000, peopl, receiv, wildfir, evacu, order,...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>photo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[just, got, sent, photo, rubi, alaska, smoke, ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature extraction**"
      ],
      "metadata": {
        "id": "e-dk0tNtplCB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wNyWemLf1ejq"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "text_vectorizer = TfidfVectorizer()\n",
        "keyword_vectorizer = TfidfVectorizer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OuX914qGkNOJ"
      },
      "outputs": [],
      "source": [
        "#Vectorizing the text using Tfidf\n",
        "\n",
        "joined_train = train_df['text'].apply(' '.join)\n",
        "keyword_train_vectors = keyword_vectorizer.fit_transform(train_df.keyword)\n",
        "text_train_vectors = text_vectorizer.fit_transform(joined_train)\n",
        "\n",
        "joined_test = test_df['text'].apply(' '.join)\n",
        "keyword_test_vectors = keyword_vectorizer.transform(test_df.keyword)\n",
        "text_test_vectors = text_vectorizer.transform(joined_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "luNCgdNmkfUp"
      },
      "outputs": [],
      "source": [
        "#Creating the feature matrix and splitting into training and validation data\n",
        "\n",
        "from scipy.sparse import csr_matrix, hstack\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(hstack([keyword_train_vectors, text_train_vectors]), train_df[\"target\"], random_state=12)\n",
        "X_test = hstack([keyword_test_vectors, text_test_vectors])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Testing base models**"
      ],
      "metadata": {
        "id": "ruwW-QrzqDlZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLzGU-_cKVHv"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "def get_classifier_cv_score(model, X, y, scoring, cv):\n",
        "  scores = cross_validate(model, X, y, cv=cv, scoring=scoring, return_train_score=True)\n",
        "    \n",
        "  mean_train_score = scores[\"train_score\"].mean()\n",
        "  mean_val_score = scores[\"test_score\"].mean()\n",
        "    \n",
        "  return mean_train_score, mean_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2h08hUvGKXvK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "603f4c39-f454-4c08-86b9-af3da0e73cee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sorted Average Results Based on Validation Score:\n",
            "\n",
            "BernoulliNB() : training score  = 0.879\n",
            "BernoulliNB() : validation score = 0.752\n",
            "\n",
            "\n",
            "LinearSVC() : training score  = 0.985\n",
            "LinearSVC() : validation score = 0.748\n",
            "\n",
            "\n",
            "RidgeClassifier() : training score  = 0.962\n",
            "RidgeClassifier() : validation score = 0.747\n",
            "\n",
            "\n",
            "SGDClassifier(random_state=12) : training score  = 0.967\n",
            "SGDClassifier(random_state=12) : validation score = 0.747\n",
            "\n",
            "\n",
            "LogisticRegression() : training score  = 0.862\n",
            "LogisticRegression() : validation score = 0.739\n",
            "\n",
            "\n",
            "MultinomialNB() : training score  = 0.842\n",
            "MultinomialNB() : validation score = 0.739\n",
            "\n",
            "\n",
            "MLPClassifier(hidden_layer_sizes=(10,)) : training score  = 0.997\n",
            "MLPClassifier(hidden_layer_sizes=(10,)) : validation score = 0.725\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import RidgeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "scores_tup = []\n",
        "\n",
        "model_list=[RidgeClassifier(), LogisticRegression(), BernoulliNB(), MultinomialNB(), SGDClassifier(random_state=12), LinearSVC(), MLPClassifier(hidden_layer_sizes=(10,), max_iter = 200)]\n",
        "\n",
        "for models in model_list:\n",
        "    \n",
        "            training_score, validation_score = get_classifier_cv_score(models, X_train, Y_train, scoring='f1', cv=10);\n",
        "     \n",
        "            scores_tup.append([models, training_score, validation_score]);\n",
        "\n",
        "            scores_tup=sorted(scores_tup, key = lambda x: x[2], reverse =True )\n",
        "\n",
        "print(\"Sorted Average Results Based on Validation Score:\\n\")\n",
        "\n",
        "for scores in scores_tup:\n",
        "                \n",
        "        print(scores[0], \": training score  = {:.3f}\".format(scores[1]))\n",
        "        print(scores[0], \": validation score = {:.3f}\".format(scores[2]))\n",
        "        print(\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Optimizing the top three models using grid search**"
      ],
      "metadata": {
        "id": "StcsY9TcqPCA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "srGzBaRbKcCK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4076fea5-c02a-48fb-b9b2-4dd3b3e0535f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 5 candidates, totalling 50 fits\n",
            "{'alpha': 0.1}\n",
            "0.7658112152963166\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "#Optimization of BernoulliNB\n",
        "\n",
        "bnb_params = {'alpha': [0.001, 0.01, 0.1, 1, 10]}\n",
        "\n",
        "bnb_grid = GridSearchCV(BernoulliNB(), bnb_params, scoring='f1', cv=10, verbose=1)\n",
        "bnb_grid.fit(X_train, Y_train)\n",
        "\n",
        "print(bnb_grid.best_params_)\n",
        "print(bnb_grid.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpVeoKcRKfjp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c582f2e2-2128-4196-93da-b6cf736372a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
            "{'alpha': 1, 'fit_intercept': False}\n",
            "0.7497336610459008\n"
          ]
        }
      ],
      "source": [
        "#Optimization of RidgeClassifier\n",
        "\n",
        "ridge_params = {'alpha': [0.001, 0.01, 0.1, 1, 10],\n",
        "                'fit_intercept': [True, False]}\n",
        "\n",
        "ridge_grid = GridSearchCV(RidgeClassifier(), ridge_params, scoring='f1', cv=10, verbose=1)\n",
        "ridge_grid.fit(X_train, Y_train)\n",
        "\n",
        "print(ridge_grid.best_params_)\n",
        "print(ridge_grid.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Optimization of LinearSVC\n",
        "\n",
        "lsvc_params = {'C': [0.001, 0.01, 0.1, 1, 10],\n",
        "               'loss': ['hinge', 'squared_hinge'],\n",
        "               'fit_intercept': [True, False],\n",
        "               'max_iter': [4000]}\n",
        "\n",
        "lsvc_grid = GridSearchCV(LinearSVC(), lsvc_params, scoring='f1', cv=10, verbose=1)\n",
        "lsvc_grid.fit(X_train, Y_train)\n",
        "\n",
        "print(lsvc_grid.best_params_)\n",
        "print(lsvc_grid.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eC8M3axrhcGe",
        "outputId": "5d173a52-6337-4694-e020-198d803df899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n",
            "{'C': 1, 'fit_intercept': False, 'loss': 'hinge', 'max_iter': 4000}\n",
            "0.7518336541954891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Retraining of the best model with optimized parameters**"
      ],
      "metadata": {
        "id": "zTtlp3KlqnYD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RLeOcVYmKhqK"
      },
      "outputs": [],
      "source": [
        "# Retrain BernoulliNB with optimized hyperparameters\n",
        "\n",
        "best_model = BernoulliNB(alpha=0.1)\n",
        "\n",
        "best_model.fit(X_train, Y_train)\n",
        "Y_pred = best_model.predict(X_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Viewing the results of the prediction on the validation set**"
      ],
      "metadata": {
        "id": "DYlaRoe2rfin"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fmQCL0NsKkZx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2835934-a784-478d-8b6b-77adf26115e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.83      1109\n",
            "           1       0.77      0.71      0.74       795\n",
            "\n",
            "    accuracy                           0.79      1904\n",
            "   macro avg       0.79      0.78      0.78      1904\n",
            "weighted avg       0.79      0.79      0.79      1904\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(Y_val, Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_mat(y_actual, y_pred, labels, title=''):\n",
        "  mat = confusion_matrix(y_actual, y_pred)\n",
        "\n",
        "  sns.heatmap(mat, square=True, cmap=\"viridis\", annot=True, xticklabels = labels, yticklabels = labels, fmt='g')\n",
        "  plt.xlabel('predicted value')\n",
        "  plt.ylabel('true value');\n",
        "  plt.title(title);"
      ],
      "metadata": {
        "id": "qlvB5nTlrUxS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_mat(Y_val, Y_pred,[0,1],'Confusion Matrix');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "vZaHaEszrYNK",
        "outputId": "b56130cf-678c-4b10-f51b-67ce2c160ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating the submission file for the Kaggle competition**"
      ],
      "metadata": {
        "id": "CddVNIiurmcy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6voHwWoIKnBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9edea606-aca5-41e4-957a-a223ac59acc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       0\n",
              "1   2       0\n",
              "2   3       1\n",
              "3   9       0\n",
              "4  11       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c120021b-5d62-420b-bcf3-21bb35a42468\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c120021b-5d62-420b-bcf3-21bb35a42468')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c120021b-5d62-420b-bcf3-21bb35a42468 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c120021b-5d62-420b-bcf3-21bb35a42468');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "submission = pd.read_csv(\"/content/ENEL645/sample_submission.csv\")\n",
        "submission[\"target\"] = best_model.predict(X_test)\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PASvpMKGKsbk"
      },
      "outputs": [],
      "source": [
        "submission.to_csv(\"submission.csv\", index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "645_ProjectV3.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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