Fake_News_Detection_Model.ipynb

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      "name": "Fake News Detection Model.ipynb",
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      "source": [
        "## **Reading the data**"
      ]
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        "outputId": "29903cb4-19fb-4335-ac07-d7f139c3cbbc"
      },
      "source": [
        "import pandas as pd\r\n",
        "train = pd.read_csv('/content/data/train.csv')\r\n",
        "train"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>author</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
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      "source": [
        "# Firstly, we fill all the null spaces with a space\r\n",
        "train = train.fillna(' ')\r\n",
        "train"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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      "metadata": {
        "id": "kZqvE5YmbhMc"
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      "source": [
        "We can see that the features ‘title’, ‘author’ and ‘text’ are important and all are in text form. So, we can combine these features to make one final feature which we will use to train the model and we will call the feature as ‘total’."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "train['total'] = train['title'] + ' ' + train['author'] + ' ' + train['text']\r\n",
        "train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "          id  ...                                              total\n",
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              "2          2  ...  Why the Truth Might Get You Fired Consortiumne...\n",
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              "4          4  ...  Iranian woman jailed for fictional unpublished...\n",
              "...      ...  ...                                                ...\n",
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              "20798  20798  ...  NATO, Russia To Hold Parallel Exercises In Bal...\n",
              "20799  20799  ...  What Keeps the F-35 Alive David Swanson   Davi...\n",
              "\n",
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          "metadata": {
            "tags": []
          },
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-_qNvGucSA2"
      },
      "source": [
        "## **Pre-processing / Cleaning the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tK_-hEkcm3V"
      },
      "source": [
        "import nltk\r\n",
        "from nltk.corpus import stopwords\r\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWxpF3mzdCld"
      },
      "source": [
        "For preprocessing the data, we need these libraries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvgEDehGdaxz"
      },
      "source": [
        "**Stopwords**: Stop words are those common words that appear in a text many times and do not contribute to machine’s understanding of the text.\r\n",
        "We don’t want these words to appear in our data. So, we remove these words. All these stopwords are stored in the ntlk library in different languages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MR8N7TDNdex1",
        "outputId": "a914e909-1f19-43cc-d3d2-ca0a74a0d0fc"
      },
      "source": [
        "nltk.download('stopwords')\r\n",
        "stop_words = stopwords.words('english')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8Ms-TQxeNVB"
      },
      "source": [
        "**Tokenization**: Word tokenization is the process of splitting a large sample of text into words."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbYXYExqf4-G"
      },
      "source": [
        "**Lemmatization**: Lemmatization is the process of grouping together the different inflected forms of same root word so they can be analysed as a single item."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-VEoqizePmP"
      },
      "source": [
        "lemmatizer = WordNetLemmatizer()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVc-RSnrgH_H"
      },
      "source": [
        "The code below is for lemmatization for our train data which excludes stopwords at the same time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NvlXSgrgNab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "4e2fbc6e-145d-4e1b-97dc-1da0ad982f94"
      },
      "source": [
        "import re\r\n",
        "nltk.download('punkt')\r\n",
        "nltk.download('wordnet')\r\n",
        "for index,row in train.iterrows():\r\n",
        "  filter_sentence=' '\r\n",
        "  sentence = row['total']\r\n",
        " # Cleaning the sentence with regex\r\n",
        "sentence = re.sub(r'[^\\w\\s]', '', sentence)\r\n",
        " # Tokenization\r\n",
        "words = nltk.word_tokenize(sentence)\r\n",
        "# Stopwords removal\r\n",
        "words = [w for w in words if not w in stop_words]\r\n",
        "# Lemmatization\r\n",
        "for words in words:\r\n",
        "  filter_sentence = filter_sentence  + ' ' + str(lemmatizer.lemmatize(words)).lower()\r\n",
        "train.loc[index, 'total'] = filter_sentence\r\n",
        "train = train[['total', 'label']]\r\n",
        "train"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "                                                   total  label\n",
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              "20797  Macy’s Is Said to Receive Takeover Approach by...      0\n",
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSXax5Uow0Fi"
      },
      "source": [
        "X_train = train['total']\r\n",
        "Y_train = train['label']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8FPn91r_kdO"
      },
      "source": [
        "Finally, we have pre-processed the data but it is still in text form and we can’t provide this as an input to our machine learning model. We need numbers for that so we will use Vectorizers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWiT0D0x_ntW"
      },
      "source": [
        "## **Vectorizer**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxxiKPLN_xxO"
      },
      "source": [
        "For converting this text data into numerical data, we will use two vectorizers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmh8etid_ykz"
      },
      "source": [
        "**Count** **Vectorizer**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVeHj270_4TB"
      },
      "source": [
        "In order to use textual data for predictive modelling, the text must be parsed to remove certain words — this process is called tokenization. These words need to then be encoded as integers, or floating-point values, for use as inputs in machine learning algorithms. This process is called feature extraction (or vectorization)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOH7V_Gu_8KX"
      },
      "source": [
        "**TF-IDF** **Vectorizer**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnu-AG4t_-qP"
      },
      "source": [
        "TF-IDF stands for Term Frequency — Inverse Document Frequency. It is one of the most important techniques used for information retrieval to represent how important a specific word or phrase is to a given document."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Asu0WHTT4Iez"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\r\n",
        "from sklearn.feature_extraction.text import CountVectorizer\r\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\r\n",
        "count_vectorizer = CountVectorizer()\r\n",
        "count_vectorizer.fit_transform(X_train)\r\n",
        "freq_term_matrix = count_vectorizer.transform(X_train)\r\n",
        "tfidf = TfidfTransformer(norm = \"l2\")\r\n",
        "tfidf.fit(freq_term_matrix)\r\n",
        "tf_idf_matrix = tfidf.fit_transform(freq_term_matrix)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DH8Acz9rALgA"
      },
      "source": [
        "The code written above will provide a matrix representing your text. It will be a sparse matrix with a large number of elements in Compressed Sparse Row format."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yy388cTcAOF9"
      },
      "source": [
        "## **Modelling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M9tizddAQb0"
      },
      "source": [
        "Now, we have to decide which classification model will be the best. First, we will split the data and then train the model to predict how accurate our model is."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4-Lz7p2-8cP"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(tf_idf_matrix, Y_train, random_state=0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXNTOlENATeC"
      },
      "source": [
        "**Logistic** **Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hc3BPjJ7_Aim",
        "outputId": "ffc8bbac-0a8f-413f-f533-18b766f75084"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "logreg = LogisticRegression()\r\n",
        "logreg.fit(X_train, y_train)\r\n",
        "Accuracy_1 = logreg.score(X_test, y_test)\r\n",
        "print(Accuracy_1)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9526923076923077\n",
            "CPU times: user 5.9 s, sys: 6.57 s, total: 12.5 s\n",
            "Wall time: 6.4 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GStertSUAadC"
      },
      "source": [
        "**Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPWi55ds_H10",
        "outputId": "311fe0e3-74ab-414b-886a-0a316c9a0afd"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.naive_bayes import MultinomialNB\r\n",
        "NB = MultinomialNB()\r\n",
        "NB.fit(X_train, y_train)\r\n",
        "Accuracy_2 = NB.score(X_test, y_test)\r\n",
        "print(Accuracy_2)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.835\n",
            "CPU times: user 79 ms, sys: 0 ns, total: 79 ms\n",
            "Wall time: 82 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya5NpHL1AeSe"
      },
      "source": [
        "**Decision Tree Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsdgQ3ID_VlN",
        "outputId": "76d8c31a-b918-40c4-90f4-721f75fe3d6d"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "clf = DecisionTreeClassifier()\r\n",
        "clf.fit(X_train, y_train)\r\n",
        "Accuracy_3 = clf.score(X_test, y_test)\r\n",
        "print(Accuracy_3)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9605769230769231\n",
            "CPU times: user 33.4 s, sys: 27.6 ms, total: 33.4 s\n",
            "Wall time: 33.6 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVWMdy9lCOoj"
      },
      "source": [
        "**KNeighborsClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlLk2PlbAtKA",
        "outputId": "8d3d3a83-9b39-4c36-b539-652d2be8afe6"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "kn = KNeighborsClassifier(n_neighbors=3)\r\n",
        "kn.fit(X_train,y_train)\r\n",
        "Accuracy_4 = kn.score(X_test, y_test)\r\n",
        "print(Accuracy_4)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8115384615384615\n",
            "CPU times: user 15.3 s, sys: 464 ms, total: 15.8 s\n",
            "Wall time: 15.8 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0twiJJCCSgu"
      },
      "source": [
        "**SGDClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7n04WR92BT6b",
        "outputId": "e9e91e0e-afc7-42e8-a86b-e981dc3045f4"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.linear_model import SGDClassifier\r\n",
        "sgd = SGDClassifier()\r\n",
        "sgd.fit(X_train,y_train)\r\n",
        "Accuracy_5 = sgd.score(X_test, y_test)\r\n",
        "print(Accuracy_5)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9728846153846153\n",
            "CPU times: user 344 ms, sys: 116 ms, total: 460 ms\n",
            "Wall time: 330 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxlnt9iSCWxZ"
      },
      "source": [
        "**RandomForestClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFNB-9q5BoZ8",
        "outputId": "d47eee3f-9e4d-43cf-b9db-04893a00ea65"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "rfc = RandomForestClassifier()\r\n",
        "rfc.fit(X_train,y_train)\r\n",
        "Accuracy_6 = rfc.score(X_test, y_test)\r\n",
        "print(Accuracy_6)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9348076923076923\n",
            "CPU times: user 1min 3s, sys: 39.9 ms, total: 1min 3s\n",
            "Wall time: 1min 3s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmBc7oSvCbdE"
      },
      "source": [
        "**Support vector machine**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFbmXEjBB5i0",
        "outputId": "374a6ea2-2039-44af-cdd4-8d2288ebdb49"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.svm import SVC\r\n",
        "svc = SVC(C=1.0, kernel='linear',random_state=123)\r\n",
        "svc.fit(X_train,y_train)\r\n",
        "Accuracy_7 = rfc.score(X_test, y_test)\r\n",
        "print(Accuracy_7)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9348076923076923\n",
            "CPU times: user 5min 15s, sys: 140 ms, total: 5min 15s\n",
            "Wall time: 5min 16s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbNsKyEnCoUl"
      },
      "source": [
        "## **Plotting different Model vs their** **Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "N-P20ypaDBlz",
        "outputId": "001e1726-f165-465d-bf09-279b0bb26572"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "# x axis values \r\n",
        "x = ['LR','NB','DT','KN','SGD','RF','SVM']\r\n",
        "# corresponding y axis values \r\n",
        "y = [Accuracy_1,Accuracy_2,Accuracy_3,Accuracy_4,Accuracy_5,Accuracy_6,Accuracy_7] \r\n",
        "# plotting a graph \r\n",
        "plt.plot(x, y, color='black', linestyle='dashed', linewidth = 3, marker='o', markerfacecolor='red', markersize=12,) \r\n",
        "# naming the x-axis \r\n",
        "plt.xlabel('Model') \r\n",
        "# naming the y-axis \r\n",
        "plt.ylabel('Accuracy') \r\n",
        "# plot title \r\n",
        "plt.title('Fake News Detection Model') \r\n",
        "# function to show the plot \r\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FQoLeOnSoZmVlBXxMc3qysrJ08BVXaH3Qv4CmgWa5//4FtFl8vH0mYbJx40YFFNC6detqbm5uSM6Tk5Ojd9xxh+9cgF566aV68ODBkJzPFA1Yo8V9hxe3ItpegSaIvLw8vXXoUO1brZoeA+ctKPQ6BtrH/ULKy8sL8G02p+vJJ59UQONBE2JjNQa0ijv/6KOPeh1ehfH666/7vrCHDBkS0nPl5eXpI488UiBJtG/fXnfv3h3S85r/KilBeF1JHXbWajdyTZkyBXC6lX5//Hj++vLLZLrzqampXoZWoYSq/qEoIsJf/vIXXnrpJd+yDRs2FIjBeKfCJQhrtRuZvvvuOzZt2gRAfHw8V155JX379vWt/+KLL8jJyfEqvApDVfn6669986FOEPlGjRrFBx98QGxsLM8//zzDhg0Ly3lNySrcgEHWajcyffLJJ77pK664gurVq5OUlESTJk1IS0vj8OHDrFy5kksuucTDKMs/EWHbtm2sWbOGZcuW8Zvf/CZs57755pvp0KFDgcdgjbcq3B2EtdqNTP4J4pprrgGcLyv/u4g5c+aEPa6KKC4uji5duvDQQw/5emQNlzZt2vzqnEeOHCnwVJUJnwqXIKzVbuTZunUr69evB6BKlSpcddVVvnWWICq2kydPkpKSQp8+fRg7dqzX4VQ4FS5BWKvdyHPWWWfx1Vdf8eCDD/I///M/1KhRw7fusssuI8b9vFavXm2jk1Uwf/jDH1i0aBG5ubncfPPNvPbaa16HVLEU93hTtL0Cfcx1xYoV2iIhodhHXPNfR0GbV6umK1euDOxZMRMynTt3VkAbNWpkn0cIff/99zpp0iT95ZdfvA7FJz09Xdu1a1fgMdjHHnvMHj8PIuwx1/+yVrvR55VXXmHjxo2kpaWRnJzsdTjl1qRJk7j++utp2LAhjz32mNfhAHDmmWeyePHiAg8nPPvss4wcOZLc3FwPI6sYKlyCEBHGjB1L45QU2iYk8GJMDLuAbJyOop4DkqpWpXFKCmPGjg17JZ35tS5dutC2bVv7LELMv+3BqYz9ECq1a9dm7ty59Pcr7h0zZgxDhw4lMzPTw8jKvwqXIMB5SuO98eOZtGAB3w4ZQvuEBKoCZwPPAN2GDOH9CROIi4vzONLy79NPP7V6hQhw8uRJli1b5pvv2bOnh9H8WrVq1fjss8+48cYbfcs++eQT+vfvz9GjRz2MrHyrkAkCnDuJ5ORkPpg8mf3HjjFz9mxfq92FCxfa7WsY7NixgyFDhtCwYUMGDBhgXT57aPny5b5f461atYrIIULj4uL417/+xQMPPOBbNn/+fHr37s2+ffs8jKz8qrAJorDevXuTP6ZEeno6S5cu9Tii8i+/7UNubi55eXm+p5WK88MPP/DOO+/w1FNPhSG6iiWc3WucjpiYGF599VX+/Oc/+5ZlZWXZ3X6IWIJwVapUieuvv943P378eA+jqRiKahxXnN27d3PWWWcxcuRInn/+eU6csBFogymU3XsHm4jw2GOP8c4779CqVStmz55NYmKi12GVS+I85RT9OnTooGvWrDmtY6xYsYKHH36Y4cOHM2TIEOrWrRuk6ExhP/30E82bNwec5PzLL79Qp06dEvdp06YN337rDAsyc+ZMrrjiipDHWREcO3aM2rVr+/q62rNnD9EyQuPJkyepWrWq12FENRH5WlU7FLXO7iD8dO7cmUWLFjFixAhLDiGW33MrOMV7pSUHsFbVobJs2TJfcmjXrl3UJAegyOQwbtw45s2b50E05U9IE4SI9BORVBHZKiIPF7G+uYjMF5ENIrJIRJoUWl9TRNJE5M1QxmnC7+OPP/ZNX3vttQHtYwkiNPyLlyLt6aWymjZtGrfeeiv9+/cv8DdmTk3IEoSIxAJvAVcAScAwESncTeNLwFhVbY8z0txzhdY/AywJVYzGG2lpaSxfvhyA2NhYUlJSAtqvW7duvl+M3333HT/99FPIYqxIoqWCujTZ2dk89NBD5Obmkp2dzfXXX88777zjdVhRLZR3EMnAVlXdrqpZwESg8DdBEpD/82Wh/3oRuQhoCMwNYYzFUnXGrF61apUXpy/XPv30U990r169qFevXkD7xcfH061bN9+83UUER34fWGeffXaB9zfaxMXFMX/+fFq3bg04/4dHjhzJn//8Z8pLXWu4hTJBNAZ2+s2nucv8rQfyRy4fBNQQkboiEgO8DDwUwviKtXTpUs455xw6d+7Mk08+6UUI5Zr/rX9pTy8VZsVMwTds2DDee+89tm7dSu3atb0O57Q0bdqUpUuXFugi5/HHH+fBBx+0djanwOtK6oeA7iKyFuiO09tFLnA3MFNV00raWURGiMgaEVmzd+/eoAXVrFkztm/fDsC8efPYsyfQDsJNadLT030tdmNiYhg0aFCZ9rdR5kxp6tWrx/z587nssst8y15//XVuuukmli1bxs3XXkudhARiY2Kok5DALdddx6pVq6LqLiO/hCPk11JcL36n+wK6AHP85h8BHilh++pAmjv9EfATsAPYBxwBni/pfIH25hqoSy+91Nd75JtvvhnUY1dkGRkZ+umnn+rw4cP16quvLvP+eXl52rhxY99n8+WXX4YgSlMenDx5Uq+55hrf30pV0DNiY/WFmBjdBZoNugv0xZgYbZGQoLcOHapZWVleh12qrKwsvXXoUG1RrZq+GIRroYTeXEOZICoB24GWQGWc4qQ2hbapB8S4088CTxdxnFuAN0s7X7ATxOjRo31/WBdffHFQj21Oz2233eb7bB5//HGvw4laWVlZmpOT43UYIZWTk6MjRozQqqBdodhu/o+B9omP11uHDo3orsTz8vL01qFDtW+1akG7Fk8ShHNergS+B7YBj7nLngYGutPXAP9xt/kHUKWIY3iSIPbu3auVKlXyfRFt3749qMc3p27OnDn6wAMP6MyZM/X48eNehxO1Jk+erImJiTpw4ED9+OOPvQ4nZJYvX66N4uJKHQPmGGiLhISIHnMk0PFsynItJSWISqdZQlUiVZ0JzCy07Am/6U+ATwrvV2j7D4APQhBeierVq0ffvn2ZMWMGABMnTuSRRx4JdximCH369KFPnz5ehxH1FixYwOHDh5k2bRrnn39+mR8YiBbvvPIK/5ubS0Ip2yUAdx4/zv8MG0aXYh73feaZZzjjjDN88ydPnuS+++4LOJY33nijQOO+n3/+mccffzygfatUqcKxPXu4OyMjoGsZmZHB2y+/TPKkSQHH9yvFZY5oewX7DkJV9aOPPvLdQbRt2zbox69ovvrqK83MzPQ6DONq1aqV7+97wYIFXocTMrWrVdNdpfzizn+lgVbxG72u8Cs1NbXAsY8cOVLstkW9jhw5UmD/1NTUgPetXr16ma+lTkJCqe8PNqLcqRk4cCDVqlUDYNOmTWzYsMHjiKLXnj17uPTSS2nQoAG33HKLdafusV27dvH9998Dzi/TLl26eBxR6BzOyKBBgNs2ALJCGcxpKuu1HM7IOK3zhbSIKdpVr16dlJQUJkyYADg9vLZv397jqKLT1KlTycvL4/Dhw2zbto3Y2NigHPf48eMsXbqUvn372ohzZeDfevqSSy4p1x3eJcbHs+fECRoFsO0eoEblyrz0ZtG9+zRs2LDAfJUqVXj33XcDjqVKlSq/Ol6g+8fFxfH7e+4p07UkxscHHFuRiru1iLZXKIqYVFWnT5/uu8Vr1qxZRD/hEMkuu+wy3/v42muvBeWY11xzjVauXFkB3bhxY1COWVHceuutvs/jmWee8TqckLr52mv1xZiYgIplXoiJ0Vuuu87rkIsVimvBiphOXZ8+fejcuTNPPfUUX3zxhf1KPQX79u0r8It1yJAhQTt2VpZTIDB79uygHbMiKC/9LwVi5KhRjI6P53gp2x0DRletyshRo8IR1ikJ97VYgihF5cqVWb58OU8++STnnnuu1+FEpalTp/rqHLp06UKTJk1K2SMw/fr1801btxuB++GHH9ixYwcACQkJBbqlKI+Sk5PpOWAAg0v4Yj0GDImPp9fAgRH9foT7WixBmJAry8hxZeHf7cbSpUttlLkA+Xfv3bVr13I/XKeIMGbsWBqnpNA2IYEXY2LYBWTj9O3zYkwMbatVo3FKCmPGjo3oUoJwX4slCBNSBw4cYP78+b75YCaIJk2akJTk9CCfmZnJ4sWLg3bs8qwiFS/li4uL473x45m0YAHfDhlC+4QE4mNiaJ+QwJZrruHjRYt4f8KEqEiW4bwWe4qpjH744Qc2bNgQ8BgGFd3nn3/u61AvOTmZZs2aBfX4ffv29Q1DOmfOHBuGNAB5eXlUqVKFzMzMCpMgwPn1nZycTPLkyV6HctrCdS12BxGgQ4cOcckll3DWWWdxww03cPx4adVEBgoWLwU6clxZWPffZTd+/HgOHTrEggULuOCCC7wOx0QwSxABSkxM5ODBg4Dz7P306dM9jijyHTp0qMDYwMF8eilf4VHmfvzxx6CfozyqWrUqPXv2DFp7FFM+WYIIkIgwfPhw3/z48eM9jCY6xMbG8vrrr9OrVy86depEy5Ytg36O+Ph4unfv7pu3uwhjgscSRBkMGzbMNz1r1iz279/vYTSRr0aNGowcOZL58+ezdOnSkJ3HipmMCQ1LEGVw9tln06lTJwBycnIKlK+bkoXy6ZC+fftSs2ZNBg8eXOYR6iqS1NRUHnzwQaZNm8ahQ4e8DsdEAUsQZWTFTJHnvPPOY9++fUyZMoUbb7zR63Ai1qxZs3jttddISUlhxIgRXodjooAliDK67rrriIlx3rYlS5awc+dOjyOKTNu3b8fp5iX0RCQqnl/3WkVs/2BOT0gThIj0E5FUEdkqIg8Xsb65iMwXkQ0iskhEmrjLLxCR5SKy2V13fSjjLIszzjijwGDoEydO9DCayHT06FGSkpI4++yz+eMf/+hrB2G8k5OTw6JFi3zzliBMIEKWIEQkFngLuAJIAoaJSFKhzV4Cxqpqe5yhSJ9zl58AblLVNkA/4DURqRWqWMvKiplKNmPGDDIzM/nhhx+YM2cOlSqFtz2mqlpSKmTt2rUcOXIEgMaNG1u/YiYgobyDSAa2qup2Vc0CJgKFmx8nAfkdwyzMX6+q36vqf9zpdJyuzeuHMNYyGTRokK9f93Xr1rFlyxaPI4osoW4cV5z58+dz66230rhxY3uAoBD//pd69uwZ0f0NmcgRygTRGPAvoE9zl/lbDwx2pwcBNUSkrv8GIpIMVAa2FT6BiIwQkTUismbv3r1BC7w0NWvW5Oabb+bOO+9kyZIl/OY3vwnbuSPd8ePHmTnzv8OQh3Oc4y+//JIPPviA3bt32+OuhVj9gzkVXldSPwR0F5G1QHecDgl9Y1GKyJnAOOBWVc0rvLOqvquqHVS1Q/364b3BGDNmDO+88w5du3b1VVobmDlzJhnuMIdt2rShdevWYTu3f3uIuXPnhq2SPNJlZWUVaIdiCcIEKpTfbLuApn7zTdxlPqqarqqDVfW3wGPuskMAIlITmAE8pqorQhinCaKPP/7YNx3O4iWAjh07Urt2bQDS09PZtGlTWM8fqVatWuXrCv2ss86iefPmHkdkokUoE8Rq4FwRaSkilYGhwDT/DUSknojkx/AI8L67vDLwGU4FthUmR4kTJ04wY8YM33w4i5fA6drD/wkzK2ZyFK5/MCZQIUsQqpoD3AvMAbYAk1V1s4g8LSID3c16AKki8j3QEHjWXX4d0A24RUTWua+I7Xby8OHDjBs3rsI/OTNr1izfL9XzzjuPNm3ahD0G63bj16z+wZyqkD5/qKozgZmFlj3hN/0J8Ks7BFX9EPgwlLEFyz333MN7771HZmYmZ5xxBpdffrnXIXkmVCPHlYV/gliyZAnHjx8nISHBk1gixXvvvceCBQtYuHCh3UGYMrHa1dNUuXJlMjMzgYrdJiIjI4N///vfvvlw1z/ka9Kkie/OJSsry0aZw6l3uP322/noo48488wzvQ7HRBFLEKfJv9HclClTfE/wVDRZWVn8/ve/57zzzqNVq1a0bdvWs1ismMmY4LAEcZo6dOjAOeecAzhdTPi3AahIEhMT+dOf/sS3337L8uXLPW2IZQnCmOCwBHGaCg8k9NFHH3kYTWSoU6eOp+fv2hHlat8AACAASURBVLUrVatWRUSoWbMmx44d8zQer5w4cYJNmzZZexBzyixBBIH/QEIzZsywvvY9Fh8fz8yZM9mzZw+rVq2ievXqXofkicWLF9OuXTvOOOMMHn30Ua/DMVHIEkQQtG7dmgsvvBBwyuI//fRTjyMKr/xO4CJJz549qVevntdheCq//cOePXs4efKkx9GYaGQJIkgqag+vmZmZNG/enIsvvphXXnmF7Oxsr0MyLmsgZ06XlJfyyQ4dOuiaNWs8O39aWhrNmjVDVRERdu3aVSEeKZwxYwZXXXUVAC1btmTbtm3WU2gEOHjwIHXr1kVViYmJ4cCBAyQmJnodlolAIvK1qnYoal2pdxAiMsCvOwxTjCZNmtC9e3fAGY9g0qRJHkcUHoUbx0VScsjJyWHZsmU88cQT/OMf//A6nLBavHixr3K6Q4cOlhzMKQmkJfX1OAP2TAHeV9XvQhxT1Lr55ptJTExk+PDhvl/V5VlWVhZTp071zXvVero4U6dO9TXYS05O5vbbb/c4ovDxL16y7jXMqQqoiMntWXUYcCugwD+BCap6NLThBc7rIqaKaPbs2VxxxRUANGvWjB07dkTUHcTBgwepV68eeXl5iAh79+6lbt26pe9YDrRr187Xm+2cOXPo06ePxxGZSHVaRUwAqnoEp8+kicCZOIP7fCMi9wUtShN1Irl4CaB27dokJycDTrHfF1984XFE4bFnzx5fcoiLi+OSSy7xOCITrQKpgxgoIp8Bi4A4IFlVrwDOB0aFNjwTqbKzs/nss89885FWvJSvIraqXrRokW+6c+fOFb6zQnPqArmDGAK8qqrtVPWvqroHQFVPALeFNLoolpOTw5w5c9i6davXoYTEokWLOHDgAOBU0Hfq1MnjiIpWOEGUl6f2SmL1DyZYAkkQTwGr8mdEJF5EWgCo6vyQRBXlxo0bR6NGjejXrx/vvPOO1+GEhH/x0pAhQyJ22NWKOMpcUlISnTt3JjY21to/mNMSyP/qjwH/8aBz3WWlEpF+IpIqIltF5OEi1jcXkfkiskFEFolIE791N4vIf9zXzYGcL1LUr1+fvXv3AjBhwgRyc3NL2SO65OTkFGgtHqnFSwCVKlWqcKPM3X///SxfvpwDBw5w8cUXex2OiWKBJIhKqpqVP+NOVy5tJxGJBd4CrgCSgGEiklRos5dwhhVtDzwNPOfuWwd4EugEJANPikjtAGKNCL1796Z+/fqA86t1yZIlHkcUXMeOHWPw4MHUq1ePM888M+K/hCpiPQRAzZo1iYuL8zoME8UCSRB7/YYIRURSgH0B7JcMbFXV7W5SmQikFNomCcgvMF3ot74vME9VD6jqQWAe0C+Ac0aEuLg4rrvuOt98eet6o1atWowZM4bdu3ezePHiiC1eyuefIJYuXeobFtUYU7JA/mffBTwqIj+JyE7g/wF3BrBfY2Cn33yau8zfemCwOz0IqCEidQPcN6L59830ySef+EadK08qVarEueee63UYpWrSpAlJSc7Na2ZmJkuXLvU4ImOiQ6kJQlW3qWpnnF/756nqxaoarEdzHgK6i8haoDuwC6eOIyAiMkJE1ojImvwy/0jRpUsXWrRoAcChQ4eYPXu2twFVcPfffz8vvPAC69evL7eNxrZt20bnzp159NFHbahVExSBdLWBiPQH2gBV8xtDqerTpey2C2jqN9/EXeajqum4dxAiUh0YoqqHRGQX0KPQvosKn0BV3wXeBacldSDXEi4iwrBhw3juuecAp5gpJaVwCVv0yc3NJTY21uswyuzOOwO56Y1uCxYsYOXKlaxcuZL169f7+gYz5lQF0lDuHZz+mO4DBLgWaB7AsVcD54pISxGpDAwFphU6dj2/jgAfAd53p+cAfUSktls53cddFlX8i5mmTZvG0aMR0zPJKcnLy6NVq1YMHjyYCRMmWNfeEWbhwoW+aWv/YIIhkDqIi1X1JuCgqv4J6AK0Km0nVc0B7sX5Yt8CTFbVzSLytF+ldw8gVUS+BxoCz7r7HgCewUkyq4Gn3WVRpW3btrRr1w6AkydPFujYLhotW7aM7du389lnn/HAAw9EXNcaFZmqWgM5E3SBJIj8oahOiEgjIBunP6ZSqepMVW2lqmerav6X/xOqOs2d/kRVz3W3uV1VM/32fV9Vz3Ff/yzbZUWO8jSQkH/juEGDBlGpUkAllBHnxx9/ZO7cuV6HEVRbtmzhl19+AZw+qM4//3yPIzLlQSD/w6eLSC3gr8A3OL25/j2kUZUjw4YNY+rUqQwfPrzAo6/RJi8vjylTpvjm87vRjib79u3j0ksvJTU1lcTERPbt2xe1Sa4w/+KlHj16RPyjxyY6lPi/w60fmK+qh4ApIvJvoKqqHg5LdOVA8+bNWbFihddhnLYVK1awa5fzjEHdunXp0aOHtwGdgrp16/raQBw+fJiVK1eWm55OrXjJhEKJPzNUNQ+nNXT+fKYlh4qpPBQviUi5bFWdl5dXoAdX63/JBEsg96HzRWSIWI1khaWqvxr7IVr5J4jy0jZlw4YNvp51GzRo4GsUaMzpCiRB3InTOV+miBwRkaMiciTEcZVLqsr69esLlBdHg1WrVrFzp9OwvXbt2lFdhNG7d29f+fyaNWvYty+QXmMiW+HiJfstZ4IlkJbUNVQ1RlUrq2pNd75mOIIrTzZv3kzbtm254IILuP/++70Op0z87x6uvvrqqO4Arnbt2r6xK8rLKHP+Q+1a8ZIJpkAaynUr6hWO4MqT5s2b88MPPwCwadMmNm7c6HFEgSlcvBSNTy8VVt7qIT766CM2b97MG2+84Rsj3JhgCKSI6Q9+r8eB6TiDCJkyqF69eoGuNqKlTcSRI0do3749VapUoVatWvTu3dvrkE6bf4KYO3du1I8yJyIkJSVx77330rRp09J3MCZAgRQxDfB7XQ60BQ6GPrTyx7/R3IQJE8jLyyth68iQmJjI559/zp49e5g5cyaVK5c6FEjEq4ijzBlzKk6lNU0acF6wA6kI+vbt6/ti+vHHH1m+fLnHEQWuZs2adOnSxeswgiI2NpbLL7/cN19enmYyJtgCqYN4Q0T+5r7eBJbitKg2ZVS5cuUCZfjRUsxUHvXt25dWrVpx33330bVrV6/DOSWZmZn84x//YNu2bVFfTGYik5T2h1VoPOgcYIeqLgtpVKegQ4cO6v80R6RavHixrxVyvXr1SE9Pj+qngqJVXl5e1HdHsWTJEl+X3j169Ii6x6dNZBCRr1W1Q1HrAvkf8gnwoar+S1U/AlaISLWgRliBdO3alcaNncHx9u3bx7x58zyOqGiqSrdu3bj77rtZsGABubkBj+MUFaI9OUDB9g+tWpXawbIxZRZQS2og3m8+Hoj+h8c9EhMTw7Bhw3zzkVrMtHHjRpYuXcrbb79NSkqKjf0QgfzvGKz9gwmFQBJEVVU9lj/jTtsdxGnwf5pp6tSpvg7kIsnHH3/smx4wYABVq1b1MJrQO3nyZOkbRZATJ04UeMjBEoQJhUASxHERuTB/RkQuAjJCF1L5d8EFF9C3b18efvhhli9fTrVqkZVvVbVAgigPjeOKM3r0aPr06UOtWrX48ccfvQ4nYMuWLfPd1bVp04aGDRt6HJEpjwLpkvN/gY9FJB1nyNEzcIYgLZWI9ANeB2KBf6jq84XWNwP+BdRyt3lYVWeKSBzwD+BCN8axqvpcYJcU+UQkoh+t3Lx5M6mpqQAkJCTQr18/jyMKnenTp/vqgebMmcOIESM8jigwVrxkwiGQhnKrgdbASOAu4DxV/bq0/UQkFqer8CuAJGCYiBTuZvL/cIYi/S3OmNWj3eXXAlVUtR1wEXCniLQI5ILM6fPvWqN///7Ex8eXsHV0i9beXW38BxMOgbSDuAdIUNVNqroJqC4idwdw7GRgq6puV9UsYCKQUmgbBfI7/ksE0v2WJ4hIJZxK8SzAepANk4pSvAQFE8T8+fOjojL+8OHDrF69GnDuRvMfdTUm2AKpg7jDHVEOAFU9CNwRwH6NgZ1+82nuMn9PATeKSBowE7jPXf4JcBzYDfwEvKSqBwqfQERGiMgaEVmzd+/eAEKKTOnp6UycONHrMAD49ttv+fbbbwGIj48v952/tW7d2td/0ZEjR1i5cqXHEZVu6dKlvm5aLrjgAurUqeNxRKa8CiRBxPoPFuQWHQWrQ55hwAeq2gS4EhjnDnOaDOQCjYCWwCgROavwzqr6rqp2UNUO9evXD1JI4ZOTk0Pfvn1p0qQJw4cP94254KXCxUsJCQkeRhN60TjKnH/9gxUvmVAKJEHMBiaJSG8R6Q1MAGYFsN8uwL9rySbuMn+3AZMBVHU5UBWoBwwHZqtqtqruAZYBRbb0i2aVKlVCVX2vSZMmeR1SuRk5riyiLUFcffXVPPjgg5x//vnlonddE7kCSRD/D1iAU0F9F7CRgg3nirMaOFdEWopIZZxK6GmFtvkJ6A0gIufhJIi97vJe7vIEoDPwXQDnjDr+bSK8bjR39OhRX4V01apV6d+/v6fxhEu0jTLXtWtXXnnlFdatW1fuiwCNtwJ5iikPWAnswCn66QVsCWC/HOBeYI67/WRV3SwiT4vIQHezUcAdIrIe587kFnU6h3oLpzJ8M06i+aeqbijrxUWDQYMGUaVKFQDWrl3Lli2lvrUhU6NGDVauXMmOHTuYOHEi1atX9yyWcCqPo8wZEwzFJggRaSUiT4rId8AbOL/qUdWeqvpmIAdX1Zmq2kpVz1bVZ91lT6jqNHf6W1W9RFXPV9ULVHWuu/yYql6rqm1UNUlV/3q6FxqpEhMTueqqq3zzEyZM8DAaR/PmzQsMblQRRFsxkzHhUNIdxHc4dwtXqeqlqvoGTsWxCbLCxUzWdXP45SeIGjVq+O7oIlFmZqbXIZgKpKQEMRjnMdOFIvJ3t4JaStjenKIrr7ySmjWd5iDbtm3zPeNuwqdjx44sWbKE/fv3884773gdTpF+/vlnEhMT6dGjBy+88ILX4ZgKoNgEoapTVXUoTivqhThdbjQQkbdFpE+4AqwIqlatypAhQ3zzXlRWDx8+nD/96U++NhAVTWxsLF27do3osTkWLlxIZmYmixcvZubMmV6HYyqAQCqpj6vqeFUdgPOo6lqcJ5tMEPkXM02cODGs4y9s376dCRMm8NRTT3HBBRdw5Ig1Wo9E1v7BhFuZRk1R1YNu4zR7+DrIevbs6euR85dffgnr6GD+bR/69OnjK+4ykcX6XzLhFv3DapUTsbGx3HHHHdx8883MmTPHNyxpOFTExnHFOXDgAJMnT+a2225j0aJFXofj8+OPP7Jt2zbA6QIl/7FcY0IpkO6+TZg888wzYT/njz/+6KsUr1SpEgMHDixlj/Lt2Wef5ZVXXgGgevXqYU3UJfG/o7z00kupXDlYvd0YUzy7g6jg/O8eLrvssgrf8Vuktoew+gfjBUsQFZwVLxXUtWtX3/CqqampETHKnKpa/YPxhCWICHX8+HEmTpzI4cOHQ3aOnTt3smLFCsCpA7n66qtDdq5oER8fX2B8hUi4i9i6dStpaWkA1KxZkwsvvLCUPYwJDksQEehPf/oTDRs2ZNiwYXz66achO8+UKVN807169aJu3bohO1c08R9iNRIShP/dQ/fu3alUyaoOTXhYgohA1atX5/jx40BoG835Fy+V95HjysK/HuKLL77wfJS5o0ePkpiYCNj40ya8pLz0+9OhQwdds2aN12EERVpaGs2aNUNViYmJIS0tjTPPPDOo5zhx4gRt2rRhx44dxMTE8PPPPxONgy6FgqrSvHlz3wBOS5cu5dJLL/U0ptzcXNatW0ejRo2C/rdgKjYR+VpVixxvx+4gIlCTJk185eB5eXlMnjw56OeoVq0a27dvZ9WqVbz11luWHPxE4ihzsbGxXHTRRZYcTFhZgohQ4RhISETo2LEjd911V0iOH80iLUEY44WQJggR6SciqSKyVUQeLmJ9MxFZKCJrRWSDiFzpt669iCwXkc0islFEqoYy1kgzZMgQX8dxq1atYuvWrR5HVLH4jzL3zTffcOjQIY8jMib8QpYgRCQWZ2S4K4AkYJiIJBXa7P9wRpr7Lc6QpKPdfSsBHwJ3qWoboAfgbU1hmNWpU6fAcJKRMJBQRVK7dm2efPJJPvzwQ9LT06lVq1bYY8jNzeXuu+/mww8/ZNeuwsO5GxN6obyDSAa2qup2Vc0CJgKFhylTIL9nuEQg3Z3uA2xQ1fUAqrpfVSvcYEX+xUwfffRR0AYS+uMf/8iYMWPYu3dvUI5XXj3xxBPccMMNNGjQwJPzr1+/nrfffpvf/e53dOjQwQaSMmEXygTRGNjpN5/mLvP3FHCjiKQBM4H73OWtABWROSLyjYj8MYRxRqwBAwb4xoVOTU1l3bp1p33MPXv28PLLL3PXXXfRuHFjDh48eNrHNKFRuPW0iI3XZcLL60rqYcAHqtoEuBIYJyIxOJ0IXgrc4P47yB3RrgARGSEia0RkTXn8NVytWjUGDRrkmw9GZfVnn31GXl4eAJ07d6Z27dqnfUwTGv4Jwto/GC+EsknmLqCp33wTd5m/24B+AKq63K2Irodzt7FEVfcBiMhM4EJgvv/Oqvou8C447SBCcA2eu/HGG9mzZw/Dhw8PSlcY1jiu7HJycli9ejV5eXlccsklYTlndnY2S5cu9c1b/0vGE6oakhdO8tkOtAQqA+uBNoW2mQXc4k6fh1MHIUBt4BugmnucL4D+JZ3voosuUlOyvXv3amxsrOLU/WhaWprXIUW8+fPna61atRTQ3r17h+28X331le9zat68uebl5YXt3KZiAdZoMd+rIStiUtUc4F5gDrAF52mlzSLytIjkDzowCrhDRNYDE9xkoap6EHgFWA2sA75R1RmhirWimDp1qm8o04svvpjGjQtXCZnCWrVq5XvEdenSpb4uUELN6h9MJAhpr1+qOhOn8tl/2RN+098CRd6zq+qHOI+6miD5+OOPfdNWvBSYJk2a0KZNGzZv3kxWVhaLFy/myiuvLH3H02TjP5hI4HUltSmDvLw8Fi9ezNq1a8u87/79+5k//79VOEOGDAlmaOVauFtVnzx5kmXLlvnmrYLaeMUSRJSYNWsWzZs3p0ePHrzwwgtl3n/atGm+4qXOnTvTtGnTUvYw+cKdIFasWMHJkycBp4jLigKNVyxBRImmTZv6Bo2ZNm0aR48eLdP+/sVLNnJc2YR7lDkrXjKRwhJElGjbti3t2rUDICMjg88//zzgfTMzM1m1apVv3hJE2YR7lLn77ruPyZMnM3LkSBvlz3jKEkQUOdUeXqtUqUJaWhrTpk3jiSeeoHnz5qEIr1zzL2aaPXt2SM9Vr149rr32WkaPHl3gvMaEmw0YFEV27NhBy5YtAWd8gN27d9s4DmGyZcsWkpKcviZr1qzJvn37fL3tGhPNbMCgcqJFixa+lry5ubkF6hVMaLVu3dpXsX/kyBFWrlzpcUTGhJ4liCgTjoGEzK+JCAMHDqRnz548//zztGjRIiTnWbdunedjYBuTz4qYoszevXs588wzfY+s/vDDDyV+Wb322mucddZZ9OnTx/ckjjk1qhrSFs2HDh2ibt26xMfH06NHDz7//HNiY2NDdj5jwIqYypX69esXqLicOHFisdseOXKEhx9+mJSUFBo0aGDjP5ymUHd3sWTJEvLy8jh+/Dg///yzJQfjOUsQUSjQYqYZM2aQmZkJwNlnn20V2hHO2j+YSBPSvphMaKSkpNCpUycGDRrE0KFDi93OGseF1pEjR6hZs2bpGwaocAd9xnjN6iDKqWPHjlG/fn1flw3fffcdv/nNbzyOKvodPXqUJ598kjlz5nDo0CHS0tKCUvS0d+9e39CmlSpV4uDBg77RBI0JpZLqIOwOopyaMWOGLzm0a9fOkkOQVKtWjQ8++MA3VOvGjRtp3779aR938eLFvunk5GRLDiYiWB1EOeU/cpwVLwVPbGwsl19+uW8+WN1uWPGSiUSWIMqB1NRU/v3vf/vmjx8/zowZ/x1fyRJEcIWid1cbf9pEopAmCBHpJyKpIrJVRB4uYn0zEVkoImtFZIOIXFnE+mMi8lAo44xW6enpXHTRRbRu3Zpbb73V18Bq1qxZZGRkAJCUlOTrIsIER58+fXzTwRhlLj09ndTUVMDpN6tLly6ndTxjgiVkCUJEYoG3gCuAJGCYiBT+pvo/nKFIfwsMBUYXWv8KzrjVpggNGzbkl19+AWDfvn188cUXgD29FGr5o8wBvlHmTof/460XX3wx8fHxp3U8Y4IllHcQycBWVd2uqlnARCCl0DYK5D8nmAik568QkauBH4DNIYwxqsXGxhZ4zHX8+PFkZ2cXKPawBBEawSxmqlOnDn369KFatWpWvGQiSsgecxWRa4B+qnq7O/87oJOq3uu3zZnAXKA2kABcpqpfi0h1YB5wOfAQcExVXyriHCOAEQDNmjW7KNQDuUSib775hosuuoh4IA/IFqFm1aq0S0qiVqNGTJ06lZgYq2oKtrlz5/qSRKtWrXxFRKcjKyuLzMxMatSocdrHMiZQkdzVxjDgA1VtAlwJjBORGOAp4FVVPVbSzqr6rqp2UNUOFbGVcHZ2Nm/89a80FOEJYDuQqcrmjAwGrF3LxgULuP2GG6zztxDo1q2bryjo+++/Z8eOHad9zMqVK1tyMBEllAliF+A/8HETd5m/24DJAKq6HKgK1AM6AS+KyA7gf4FHReRejI+qcudNN7F72jS2qfIw0AinYUsj4A95eWw6fpxdn3/OnTfdRHlpEBkpqlatGtZR5ozxQigTxGrgXBFpKSKVcSqhpxXa5iegN4CInIeTIPaqaldVbaGqLYDXgL+o6pshjDXqrFq1ioXTpzPlxAkSitkmAfg0I4OF06ezevXqcIZXIeQXMZ1zzjnWsZ4pl0KWIFQ1B7gXmANswXlaabOIPC0iA93NRgF3iMh6YAJwi9pP3YC8/fLL3J2RUWxyyJcAjMzI4O2XXw5HWBXKjTfeyNatW/nPf/7D7bffXub9VZWePXtyzz33MGXKFCsKNBHH+mKKUnUSEth04gSNAth2F9A+IYH9x0qs0jFhlpqaSuvWrQFITExk//79didiwi6SK6nNKTqckUGDALdt4G5vIot/6+kePXpYcjARxxJElEqMj2dPgNvucbc3kcW61zCRzhJElBrYvz8fBdi+4aOYGAb27x/iiCquLVu28Oqrr9KvXz/27dsX0D55eXksWrTIN28d9JlIZAkiSo0cNYrR8fGU1gvQMWB01aqMHDUqHGFVSLfffju///3vmTNnDvPmzQton02bNvmSSf369X1ddxgTSSxBRKnk5GR6DhjA4BKSxDFgSHw8vQYOpGPHjuEMr0I5lW43Ctc/WGt3E4nsrzJKiQhjxo6lcUoKbRMSeDEmhl1ANs5TSy/GxNC2WjUap6QwZuzYoIx6ZormnyDmzp0bUKNEG3/aRANLEFEsLi6O98aPZ9KCBXw7ZAjtExKIj4mhfUICW665ho8XLeL9CROIi4vzOtRyrUOHDtSpUweA3bt3s3HjxhK3z83NLdADrCUIE6lsyNEoJyIkJyeTPHmy16FUWLGxsVx22WVMdj+DOXPmlDgM6dq1azl8+DAAjRo14txzzw1LnMaUld1BGBMEZamH8O/2pFevXlb8ZyKWtaQ2JgjS0tJo2tTpm7Jy5cocOHCAhITiO0LZuXMnCxcupGXLlnTt2jVcYRrzK9aS2pgQK+soc02bNuWmm26y5GAimiUIY4IkmKPMGRMJLEEYEyT+CeLLL7/0MBJjgsMShDFB0q1bNx555BEWLVrE8uXLi9xm0qRJrF27lry8vDBHZ0zZWSW1MWGSkZFB7dq1yczMpE6dOmzfvp3ExESvwzIVnGeV1CLST0RSRWSriDxcxPpmIrJQRNaKyAYRudJdfrmIfC0iG91/rSWRiXrLly8nMzMTgAYNGlhyMBEvZA3lRCQWeAu4HEgDVovINFX91m+z/8MZae5tEUkCZgItgH3AAFVNF5G2OKPSNQ5VrMaEg3WvYaJNKO8gkoGtqrpdVbOAiUBKoW0UqOlOJwLpAKq6VlXT3eWbgXgRqRLCWI0JqoMHD/LJJ5+QmprqW+bfQZ8lCBMNQpkgGgM7/ebT+PVdwFPAjSKShnP3cF8RxxkCfKOqmaEI0phge+6556hXrx7XXnstH374IQBHjx5l1apVvm26d+/uVXjGBMzrp5iGAR+oahPgSmCciPhiEpE2wAvAnUXtLCIjRGSNiKzZu3dvWAI2pjTnnnuu7yml/PYQX375JTk5OQCcf/751KtXz7P4jAlUKBPELqCp33wTd5m/24DJAKq6HKgK1AMQkSbAZ8BNqrqtqBOo6ruq2kFVO9SvXz/I4Rtzanr37u0b32HNmjXs27fPipdMVAplglgNnCsiLUWkMjAUmFZom5+A3gAich5OgtgrIrWAGcDDqroshDEaE3S1a9emU6dOAKgqX3zxRYEKaht/2kSLkCUIVc0B7sV5AmkLztNKm0XkaREZ6G42CrhDRNYDE4Bb1GmYcS9wDvCEiKxzXw1CFasxwdavXz/f9IQJE/jmm28AiImJoVu3bl6FZUyZWEM5Y0JgxYoVdOnShXggD8gCKgP1atfm09mz6dixo3XzbSKC9eZqTBhlZ2cz5rXXaAA8AWzHSRDbgfsOHeL6Xr24bfhwsrOzPY3TmNJYgjAmiFSVO2+6id3Tp7MdeBhohNMitRHw/1TZdPw4uz7/nDtvuimg8auN8YolCGOCaNWqVSycPp0pJ05Q3HBBCcCnGRksnD69wOhyxkQaSxDGBNHbL7/M3RkZxSaHfAnAyIwM3n755XCEZcwpCVlfTMZURNNmzOAvAXblfUNeHu1nzAhxRMacOruDMCaIDmdkEOjz2A3c7Y2JVJYgjAmixPh49gS47R53e2MilSUIY4JoYP/+fBQT2H+rj2JiGNi/f4gj5mTQwAAAB59JREFUMubUWYIwJohGjhrF6Ph4jpey3TFgdNWqjBw1KhxhGXNKLEEYE0TJycn0HDCAwSUkiWPAkPh4eg0cSMeOHcMZnjFlYgnCmCASEcaMHUvjlBTaJiTwYkwMu4BsnK6MX4yJoW21ajROSWHM2LHW3YaJaJYgjAmyuLg43hs/nkkLFvDtkCG0T0ggPiaG9gkJbLnmGj5etIj3J0wgLi7O61CNKZG1gzAmBESE5ORkkidP9joUY06Z3UEYY4wpkiUIY4wxRSo340GIyF7gx9M4RD1gX5DC8VJ5uQ6wa4lU5eVayst1wOldS3NVLXLM5nKTIE6XiKwpbtCMaFJergPsWiJVebmW8nIdELprsSImY4wxRbIEYYwxpkiWIP7rXa8DCJLych1g1xKpysu1lJfrgBBdi9VBGGOMKZLdQRhjjCmSJQhjjDFFqnAJQkSOFbHsKRHZJSLrRORbERnmRWxlISIqIi/7zT8kIk+50/7X852IvC0iEftZi0iuG+tmEVkvIqNEJEZE+rrL14nIMRFJdafHeh1zcfz/vkTkShH5XkSau5/JCRFpUNS2kUBEHnM/gw3u+9xJRCqJyF9E5D9+n8VjfvsU+dl5eR1F8Ytzk4hMF5Fa7vIWIpLhd23rRKSy1/H6K+JzeVJEniu0zQUissWd3iEiSwutXycim8p67oj7ID30qqpeAKQAY0Qk0ntSywQGi0i9YtbnX08S0A7oHrbIyi5DVS9Q1TbA5cAVwJOqOsddfgGwBrjBnb/J02gDICK9gb8BV6hqfgPOfUBEDgAhIl2Aq4ALVbU9cBmwE/gz0Aho534OXQH//xtFfnZhDT4w+XG2BQ4A9/it25b/d+a+sjyK8VeK+VwWAtcX2nQoMMFvvoaINHWPcd6pnt8SRCGq+h/gBFDb61hKkYPz5MKDpWxXGagKHAx5REGgqnuAEcC9EqV9YYtIN+DvwFWqus1v1fvA9SJSx5vISnQmsE9VMwFUdR9wCLgDuE9VT7rLj6rqU0UdIIo+u+VAY6+DCNCvPhdVXQIcFJFOfttdR8EEMZn/JpFhhdYFzBJEISJyIfAf94890r0F3CAiiUWse1BE1gG7ge9VdV14Qzt1qrodiAUalLZtBKoCTAWuVtXvCq07hpMkHgh7VKWbCzR1i8RGi0h34BzgJ1U9GuhBIv2zE5FYoDcwzW/x2X7FS295FFpxivpcwPnCHwogIp2BA+6P23xTgMHu9ABg+qmc3BLEfz0oIpuBlcCzXgcTCFU9AowF7i9idX4RUwMgQUSGhjW4iisb+Aq4rZj1fwNuFpEa4QupdKp6DLgI5w5gLzAJ/n979xNiVRnGcfz7g4SmooVS7lSEaBPiqo0RQgiJu0ZISqhWLcogWilBTa2K/INKFP5DDE1cTEhJGUVoRNRCGKMI+u8fhEqySBc2Pi6e9za36zvN3DvO/YO/z+6e95wz75wX7nPe55z7vCxv3kfSE+VL9FQjfTFAhsoN0zlgPvBhU1tziump+uG9URsXSY+T47O6PO9pTS8B/E7OMtYA35BZkbY5QEzYXPKow8AuSTf3ukPTtIX8Mrq11hgRl4H3gfu72amZkLQYGAcGYRbX6go53b9X0obWxoj4A9jPf3PgfSEixiPik4h4AXiavPNc0AhmEbGn3HRcIGcJ1+jjsbtU+r4QEH14/SdTGZfhiDgF/Eg+WxwmA0arg2SWoaP0EjhAXCMiDpMPRB/rdV+mIyLOk/nG6h1ryQUvA76vtfcbSXcAbwDbY0B/xRkRF4FVZPqvNi6bgCfpowW7JN0t6a6mTUuBb4FdwPbGDVNJ0VTf8hmEsStj8wzwnKS+uf6TmWRcGi89HAA2Az9ExOnK4aPAq8AHnf79vr9As+AWSc0Xc1Nln5eA/ZJ2RMSVLvVrJjaSdxbNnpW0lnzjZAx4veu9mr7G9H8O+fB9H/VxGRgRcV7Sg8AxZSn65rbfJI0y9QsG3XQbsK28/vkP8B2Z1rgAvAx8Jekv4BKwFzhbjhu4sYuIE5LGyIe3x6fav8cmGxeAQ2TKcl3twPLs6BWg47XPXWrDzMyqnGIyM7MqBwgzM6tygDAzsyoHCDMzq3KAMDOzKgcIszYoq+i+1fT5Jkm/Snq3zfP89D+FFqe9j9lscoAwa8/fwD2ShsrnFcCZHvbHbNY4QJi17wj5S2loqZQpaa6kd0rt/s8lLSnb50k6Wur67yTLPTSOWSvpi1Ln6M3ya2WznnOAMGvf28CaUn5iCVngsWEEOFFq928giylCrpHwaan3NQosgH9r9T8MLCu1gsaBR7vyX5hN4UYstWE2IxExJmkROXs40tJ8H1k8jYj4uMwcbieLJT5Utr8nqbE+xwNktc4vSzmEIfqv0J3doBwgzDpzGHiNLIk9bwbnEbA3ItZfj06ZXU9OMZl1ZjcwEhEnW7Yfp6SIJC0nVwP7EzgGPFK2r2RixcKPyLr+d5a2uZIWzn73zabmGYRZB0p55a2VpheB3aVa6EUmysaPAAfKolSfAb+U83wt6XngaFn85TK5VsHPrSc26zZXczUzsyqnmMzMrMoBwszMqhwgzMysygHCzMyqHCDMzKzKAcLMzKocIMzMrOoqRU/aDbg4zW0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72RDe3S2Dr4k"
      },
      "source": [
        "## **Result**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lf8yjmKeDup6",
        "outputId": "bff3fbc0-5c32-48e9-9fdc-fb6f69f8a444"
      },
      "source": [
        "best = max(Accuracy_1,Accuracy_2,Accuracy_3,Accuracy_4,Accuracy_5,Accuracy_6,Accuracy_7) \r\n",
        "for i in y:\r\n",
        "  if i == best:\r\n",
        "    z= y.index(i)\r\n",
        "    break\r\n",
        "print(\"The best model, we can use here is: \",x[z])\r\n",
        "print(\"It's accuracy is : \", i)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best model, we can use here is:  SGD\n",
            "It's accuracy is :  0.9728846153846153\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}