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RandomForestClassificationwithScikitLearn.ipynb
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randomforestclassificationwithscikitlearn.ipynb
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"# Data Processing\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Modelling\n",
"from sklearn.model_selection import train_test_split #Dividir o data set em conjuntos\n",
"from sklearn import metrics\n",
"from sklearn.metrics import accuracy_score, confusion_matrix #Para avaliar o modelo\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.feature_extraction.text import TfidfVectorizer #Para vetorizar o texto representar numericamente o texto\n",
"\n",
"# Tree Visualisation\n",
"from sklearn.tree import export_graphviz\n",
"from IPython.display import Image\n",
"import graphviz"
]
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"spam = pd.read_csv('spam.csv')"
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" 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..."
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},
"metadata": {},
"execution_count": 22
}
]
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{
"cell_type": "code",
"source": [
"spam.shape"
],
"metadata": {
"id": "_6RfpYT_7MeQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "19c54f91-f5c3-439d-f62d-8a13a54dd049"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(5572, 2)"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"spam['Category'].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U8tfOr0w9rgH",
"outputId": "5089e384-2e33-49d3-84f0-4137ff9b4fb1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ham 4825\n",
"spam 747\n",
"Name: Category, dtype: int64"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"messages = spam['Message']\n",
"category = spam['Category']"
],
"metadata": {
"id": "aw0LlApM-vnr"
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"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Vetorização do texto\n",
"vectorizer = TfidfVectorizer()\n",
"text_vectorizer = vectorizer.fit_transform(messages)"
],
"metadata": {
"id": "3DVYx-M7_Fi1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text_vectorizer.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "sXajo0J1_4PO",
"outputId": "085dd50b-5315-46b7-d4bc-d05f2c248ddd"
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"execution_count": null,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"(5572, 8709)"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"X_train, X_test, y_train, y_test = train_test_split(text_vectorizer, category, test_size=0.3)"
],
"metadata": {
"id": "KM7tS78YAG1u"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X_train.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "joFWMLgsBp_3",
"outputId": "b7570a22-3645-41fe-a1ae-afe104a8cee6"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(3900, 8709)"
]
},
"metadata": {},
"execution_count": 10
}
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},
{
"cell_type": "code",
"source": [
"X_test.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "PmKqq6PuBurK",
"outputId": "0378a76d-215a-43ca-b32e-25235be458b4"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
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"(1672, 8709)"
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"metadata": {},
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{
"cell_type": "markdown",
"source": [
"## Montagem e avaliação do modelo\n",
"\n",
"Primeiro, criamos uma instância do modelo Random Forest, com quantidade de árvores. Em seguida, ajustamos isso aos nossos dados de treinamento. Passamos os recursos e a variável de destino, para que o modelo possa aprender.\n"
],
"metadata": {
"id": "jvH-pCzpDj9Q"
}
},
{
"cell_type": "code",
"source": [
"rf = RandomForestClassifier(n_estimators=500)\n",
"rf.fit(X_train, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Rt0buSlOBwke",
"outputId": "2a63b74c-4268-469c-e973-01f73ff08e58"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestClassifier(n_estimators=500)"
],
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]
},
{
"cell_type": "markdown",
"source": [
"Temos um modelo treinado de Floresta Aleatória, mas precisamos descobrir se está fazendo previsões precisas."
],
"metadata": {
"id": "uNCLZmGGDyW7"
}
},
{
"cell_type": "code",
"source": [
"y_pred = rf.predict(X_test)\n",
"y_pred"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GmsK1OLHDIv_",
"outputId": "9746543b-c679-4be1-e3e1-324c833c9095"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['spam', 'ham', 'ham', ..., 'ham', 'ham', 'ham'], dtype=object)"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"source": [
"Verificamos as previsões em relação aos valores reais no conjunto de testes e contamos quantas o modelo acertou."
],
"metadata": {
"id": "gseibLs3EC9O"
}
},
{
"cell_type": "code",
"source": [
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gPHoQ4EGDK1O",
"outputId": "7f2c551d-d00d-4c05-99fb-54646bb0115f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 0.9820574162679426\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Determina a matriz de confusão\n",
"\n",
"![confusion_matrix](https://diegonogare.net/wp-content/uploads/2020/04/matrizConfusao-600x381.png)"
],
"metadata": {
"id": "L_5sSlgQE8_E"
}
},
{
"cell_type": "code",
"source": [
"confusion_matrix(y_test, y_pred)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "alvI7NvvEdjg",
"outputId": "f9bda44b-f1b3-40ae-b71e-b0c314e63e0a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1445, 0],\n",
" [ 30, 197]])"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"print(metrics.classification_report(y_test, y_pred))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8T-bm0JNGKT8",
"outputId": "94819da0-9884-412b-ddcb-85e4b407eb51"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" ham 0.98 1.00 0.99 1445\n",
" spam 1.00 0.87 0.93 227\n",
"\n",
" accuracy 0.98 1672\n",
" macro avg 0.99 0.93 0.96 1672\n",
"weighted avg 0.98 0.98 0.98 1672\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Testando o modelo Florest criado"
],
"metadata": {
"id": "mqpBNU4VHn3R"
}
},
{
"cell_type": "code",
"source": [
"test_text = ['WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.']\n",
"vectorizer_test_text = vectorizer.transform(test_text)\n",
"rf.predict(vectorizer_test_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0cn-U_CBGY6k",
"outputId": "a3e44341-65d2-4752-efa7-72aea15f2f05"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['spam'], dtype=object)"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"source": [
"## Visualizando os resultados\n",
"Podemos usar o seguinte código para visualizar nossas três primeiras árvores."
],
"metadata": {
"id": "advjkn6xEI1Q"
}
},
{
"cell_type": "code",
"source": [
"for i in range(3):\n",
" tree = rf.estimators_[i]\n",
" dot_data = export_graphviz(tree,\n",
" filled=True,\n",
" max_depth=2,\n",
" impurity=False,\n",
" proportion=True)\n",
" graph = graphviz.Source(dot_data)\n",
" display(graph)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "t59Ou8ZxDhCy",
"outputId": "28dfc832-c58d-49e8-ca70-f6f49a41ce7a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
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