RELATÓRIO DOS EXPERIMENTOS E RESULTADOS DO TCC DE GUILHERME PASSOS
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"
INTRODUÇÃO
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"\n",
">
Este trabalho tem como objetivo realizar uma análise de dados dos preços de apartamentos da cidade de Belo Horizonte e desenvolver um modelo computacional preditivo capaz de prever tais preços. Para tal, obteve-se um conjunto de dados composto por aproximadamente 57 mil anúncios de imóveis entre os meses agosto e outubro de 2018.
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"\n",
">
Alguns kernels do problema House Prices: Advanced Regression Techniques do portal Kaggle foram utilizados como referência dos experimentos mostrados a seguir. Em especial os kernels:
A análise de dados realizada neste trabalho se restringiu apenas à preços de apartamentos devido à grande quantidade de anúncios e pela dificuldade em se criar um modelo genérico para diferentes tipos de imóveis. Os resultados finais obtidos foram satisfatoriamente validados e demonstraram que a modelagem do problema realizada neste trabalho pode ser útil ao setor imobiliário da cidade.
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"\n",
">
Este relatório está dividido nas seguintes etapas:\n",
">
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"
Obtenção dos dados utilizando um web crawler.
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"
Exclusão dos dados que possuem informações inconsistentes.
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"
Organização dos conjuntos de treino e teste.
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"
Determinação das características relevantes e criação de uma distribuição geofráfica de preços utilizando as informações de latitude e longitude de cada apartamento.
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"
Transformação Box Cox nas variáveis que possuem distribuições altamente assimétricas. \n",
"
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"
Predição dos preços do conjunto teste utilizando os principais modelos de regressão da bibloteca scikit-learn. \n",
"
Os dados foram obtidos utilizando o script contido neste repositório.
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"\n",
">
Inicialmente, foram realizadas seis extrações no portal da Rede NetImóveis nos dias 05/08/2018, 12/08/2018, 21/08/2018, 25/08/2018 e 29/08/2018 a fim de se obter dados para o compor conjunto de treino dos experimentos. Posteriormente, foi realizada uma extração no dia 12/10/2018 para ser utilizada como conjunto de teste dos experimentos. Os arquvivos csv contendo os dados podem ser visualizados neste link.
2.1 EXCLUSÃO DOS DADOS QUE POSSUEM INFORMAÇÕES INCONSISTENTES
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"\n",
">
Por meio de uma análise empírica, foi possivel observar que as coordenadas geográficas com latitudes superiores a -19.788 e inferiores a -20.023 bem como aquelas com longitudes superiores a -43.878 e inferiores a -44.055 não estão dentro dos limites da cidade de Belo Horizonte.
Pode se verificar no google maps que as coordenadas mostradas acima não correspodem ao bairro e região que se referem.
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"\n",
">
Além de inconsistência nas informações de localização, alguns dados possuem outras informações incoerentes como quantidade de quartos e banheiros igual a zero ou área inferior a 10m². A função \"Drop_MisInformation\" a seguir é responsável por excluir todas essas inconsistências.
"
],
"text/plain": [
" ImovelID Preço Area Qtde_Quartos Qtde_Banheiros Bairro\n",
"4703 115490 290000 99.24 0 1 Santa Mônica\n",
"13046 32792 525000 78.00 0 1 Santa Efigênia\n",
"1472 3247325 1900000 342.00 0 5 Liberdade\n",
"10537 503016 350000 63.14 0 1 Planalto\n",
"6999 3256690 620000 160.00 0 4 Grajaú"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dados da extração do dia 05/08 que possuem quantidade de quartos igual a 0:\n",
"dataErr = data12_08.loc[(data12_08['Qtde_Quartos'] ==0 )]\n",
"# Dados inconsistentes:\n",
"dataErr.loc[:,['ImovelID','Preço','Area','Qtde_Quartos','Qtde_Banheiros','Bairro']].sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# A função Drop_Mislocation exclui os dados que possui informações de localização \n",
"# fora do território de Belo Horizonte\n",
"def Drop_MisInformation(df):\n",
" df = df.drop(df.loc[(df['Latitude'] > -19.788) | (df['Latitude'] < -20.023) | \n",
" (df['Longitude'] > -43.878) | (df['Longitude'] < -44.055)].index)\n",
" \n",
" df = df.drop(df.loc[(df['Qtde_Quartos']==0)].index)\n",
" df = df.drop(df.loc[(df['Qtde_Banheiros']==0)].index)\n",
" df = df.drop(df.loc[(df['Area']<10)].index)\n",
" return df\n",
"\n",
"# Excluindo dados inconsistentes da extração de 05/08/2018:\n",
"data05_08 = Drop_MisInformation(data05_08)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 12/08/2018:\n",
"data12_08 = Drop_MisInformation(data12_08)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 21/08/2018:\n",
"data21_08 = Drop_MisInformation(data21_08)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 25/08/2018:\n",
"data25_08 = Drop_MisInformation(data25_08)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 29/08/2018:\n",
"data29_08 = Drop_MisInformation(data29_08)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 29/08/2018:\n",
"data21_09 = Drop_MisInformation(data21_09)\n",
"\n",
"# Excluindo dados inconsistentes da extração de 29/08/2018:\n",
"data12_10 = Drop_MisInformation(data12_10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
2.2 PREPARAÇÃO DO CONJUNTO DE TREINAMENTO
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"\n",
">
A seguir irá se agrupar os dados obtidos em todas as extrações.
"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15514"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados obtidos na consolidadação das extrações do dia 05/08/2018 e 12/08/2018:\n",
"base_consolidada = pd.concat([data05_08, data12_08]).groupby('ImovelID', as_index=False, sort=False).first()\n",
"len(base_consolidada)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9191"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados novos nas extrações que foram consolidadas:\n",
"len(base_consolidada) - len(data05_08)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6220"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados duplicados nas extrações que foram consolidadas:\n",
"len(pd.concat([data05_08, data12_08])) - len(base_consolidada)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
Os resultados anteriores mostram que a extração do dia 12/08/2018 acrescenta 9191 dados novos à extração do dia 05/08/2018. Além disso, são produzidos 6220 valores repetidos na junção dessas extrações. Analisando mais minuciosamente os dados duplicados, por meio da função \"CheckingDuplicateValues\", observa-se que 130 desses 6220 (ou 2% dos duplicados) apresentaram variação no preço; o que parece aceitável. A variação percentual média dos preços que sofreram alteração entre as extrações foi de -1.93%.
"
],
"text/plain": [
" ImovelID Preco_1 Preco_2\n",
"4277 346874 255000 249000\n",
"2684 1668412 370000 346500\n",
"5532 3252772 215000 205000\n",
"3184 3245468 350000 340000\n",
"2301 341304 320000 325000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"\n",
"# Verifica dentre os dados duplicados, quais ocorreram variação no preço de uma extração para outra\n",
"def Checking_Duplicate_Values(df1,df2):\n",
" \n",
" result= pd.merge(df1[['ImovelID','Preço']],df2[['ImovelID','Preço']], on='ImovelID', how='inner')\n",
" return pd.DataFrame({\"ImovelID\":result.loc[(result.iloc[:,1]!=result.iloc[:,2])]['ImovelID'],\n",
" \"Preco_1\":result.loc[(result.iloc[:,1]!=result.iloc[:,2])]['Preço_x'],\n",
" \"Preco_2\":result.loc[(result.iloc[:,1]!=result.iloc[:,2])]['Preço_y']})\n",
"\n",
"# Dados duplicados que apresentaram variação no preço entre as extrações do dia 05/08 e 12/08/2018:\n",
"df_duplicates_01 = Checking_Duplicate_Values(data05_08, data12_08)\n",
"df_duplicates_01.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"130"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados duplicados que sofreram variação no preço entre uma extração e outra:\n",
"len(df_duplicates_01)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.09%'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Percentual de dados duplicados que apresentaram variação de preço entre uma extração e outra:\n",
"per_duplicates = len(df_duplicates_01)*100 / (len(pd.concat([data05_08, data12_08])) - len(base_consolidada))\n",
"str(round(per_duplicates,2)) + \"%\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'-1.93%'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Variação percentual média dos preços duplicados:\n",
"variação_01 = ((df_duplicates_01['Preco_2'] - df_duplicates_01['Preco_1'])/df_duplicates_01['Preco_1'])*100\n",
"str(round(variação_01.mean(),2)) + \"%\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
Ao se prosseguir com a junção das extrações da maneira que foi realizado anteriormente, observou-se que a variação média desses preço foi de -1.9%. Prosseguindo com a junção das extrações.
"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16048"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados não duplicados na consolidação das extrações dos dias 05/08, 12/08 e 21/08:\n",
"base_consolidada_01 = pd.concat([base_consolidada, data21_08]).groupby('ImovelID', as_index=False, sort=False).first()\n",
"len(base_consolidada_01)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16049"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados não duplicados na consolidação das extrações dos dias 05/08, 12/08, 21/08 e 25/08:\n",
"base_consolidada_02 = pd.concat([base_consolidada_01, data25_08]).groupby('ImovelID', \n",
" as_index=False, sort=False).first()\n",
"len(base_consolidada_02)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16225"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados não duplicados na consolidação das extrações dos dias 05/08, 12/08, 21/08 e 29/08:\n",
"base_consolidada_03 = pd.concat([base_consolidada_02, data29_08]).groupby('ImovelID', \n",
" as_index=False, sort=False).first()\n",
"len(base_consolidada_03)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16225"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados não duplicados na consolidação das extrações dos dias 05/08, 12/08, 21/08, 29/08 \n",
"# e 21/09:\n",
"base_consolidada_04 = pd.concat([base_consolidada_03, data21_09]).groupby('ImovelID', \n",
" as_index=False, sort=False).first()\n",
"len(base_consolidada_04)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21234"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de dados não duplicados na consolidação das extrações dos dias 05/08, 12/08, 21/08, 29/08, \n",
"# 21/09 e 12/10:\n",
"base_consolidada_05 = pd.concat([base_consolidada_04, data12_10]).groupby('ImovelID', \n",
" as_index=False, sort=False).first()\n",
"len(base_consolidada_05)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
Ir-se-á urilziar os 21234 dados da \"baseconsolidada05\" como o conjunto de treino dos experimentos que serão apresentados nos tópicos seguintes.
Em estatística, outlier, valor aberrante ou valor atípico, é uma observação que apresenta um grande afastamento das demais da série (que está \"fora\" dela), ou que é inconsistente. A existência de outliers implica, tipicamente, em prejuízos a interpretação dos resultados dos testes estatísticos aplicados às amostras. Existem vários métodos de identificação de outliers. Examinar-se-á o conjunto de treino a fim de se identificar tais ocorrências.
"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Otbtendo algumas biblotecas importantes para esta etapa do relatório\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt # Matlab-style plotting\n",
"from matplotlib import pyplot\n",
"from matplotlib import colors as mcolors\n",
"import seaborn as sns\n",
"color = sns.color_palette()\n",
"sns.set_style('darkgrid')\n",
"import random \n",
"from random import shuffle\n",
"from scipy import stats\n",
"from scipy.stats import norm, skew #for some statistics\n",
"from scipy.special import boxcox1p\n",
"import warnings\n",
"def ignore_warn(*args, **kwargs):\n",
" pass\n",
"warnings.warn = ignore_warn # ignora avisos das bilotecas sklearn e seaborn\n",
"pd.set_option('display.float_format', lambda x: '{:.4f}'.format(x)) # Limita a exibição de floats em 4 casa decimais\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
A função \"PlotPriceVsArea\" definida abaixo traça o gráfico da distribuição de preços versus a área do imóvel.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define os melhores limites para os eixos do gráfico\n",
"def Define_Axis_Limits(limit,up):\n",
" axis_limit = 1;\n",
" while((axis_limit * 10) <= limit):\n",
" axis_limit *= 10;\n",
" \n",
" if up: axis_limit *= 10\n",
" return axis_limit\n",
"\n",
"# Traça o gráfico da distribuição de preços versus a área do imóvel.\n",
"def PlotPriceVsArea (df):\n",
" \n",
" # Obtem a lista de regiões da base de dados\n",
" regions = df['Regiao'].loc[~df['Regiao'].isnull()].unique()\n",
" data = []\n",
" \n",
" # Separa os dados por região\n",
" for region in regions:\n",
" data.append((df['Area'].loc[df['Regiao']==region] ,\n",
" df['Preço'].loc[df['Regiao']==region]))\n",
" \n",
" # Define as cores da legenda\n",
" colors = ('gray','green','brown','blue','black','purple','olive','gold','red')\n",
" \n",
" # Formata a figura de exibição do gráfico\n",
" fig = plt.figure(figsize=(8*1.5, 6*1.5))\n",
" ax = fig.add_subplot(1, 1, 1, facecolor=\"1.0\")\n",
" \n",
" # Sepera os dados por região e cor\n",
" for data, color, region in zip(data, colors, regions):\n",
" x, y = data\n",
" ax.scatter(x,y, alpha=0.8, c=color, edgecolors='none', s=30, label=region)\n",
" \n",
" # Configura e plota o gráfico\n",
" plt.title('Distribuição dos Preços de Apartamentos por Área e Região')\n",
" plt.xlim(0,800,10)\n",
" y_lower = Define_Axis_Limits(df['Preço'].min(),False)\n",
" y_upper = Define_Axis_Limits(df['Preço'].max(),True)\n",
" plt.ylim(y_lower,y_upper)\n",
" plt.semilogy()\n",
" plt.xlabel('Área(m²)', fontsize=12)\n",
" plt.ylabel('Preço em R$', fontsize=12)\n",
" plt.legend(loc='lower right')\n",
" plt.grid(True)\n",
" plt.show()\n",
" \n",
"# Visualizando os dados:\n",
"PlotPriceVsArea(train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
O gráfico acima permite identificar alguns outliers de maneira direta, como o ponto roxo na parte inferior direita e o ponto preço na parte inferior esquerda, outros porém merecem uma análise mais detalhada. Todovia a remoção de todos eles pode afetar negativamente o desempenho dos modelos, uma vez que pode haver outros outliers no conjunto de teste também. Assim, ao invés de removê-los, ir-se-á tentar criar modelos robustos o suficiente a esses dados.
Ir-se-á observar agora a correlação entre as variáveis independentes do problema com a variável de interesse, o preço. A partir deste ponto, ir-se-á desconsiderar as informações de região, bairro e endereço; pois acredita-se com muita convicção que a coordenas geográficas por si só serão capazes de modelar a localidade dos imóveis.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Mapa de coorelação entre as variáveis do problema:\n",
"corrmat = train.iloc[:,1:12].corr()\n",
"plt.subplots(figsize=(12,9))\n",
"sns.heatmap(corrmat, vmax=0.9,annot=True, square=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
O gráfico acima indica uma baixa coorelação das variáveis Valor_IPTU, Valor_Cond, Latitude e Longitude com a variável de interesse, Preço. As duas primeiras (Valor_IPTU e Valor_Cond) podem ser tranquilamente excluídas da análise; de maneira contrária, sabe-se que a latitude e a longitude carregam informações relevantes sobre os preços e não podem ser desconsideradas. Então por que essas variáveis não apresentam correlação signiticativa como se espera?
\n",
"\n",
">
Uma possível explicação para a baixa correlação das duas primeiras variáveis citadas é o fato de vários imóveis serem isentos de IPTU, não terem taxa de condomínio, ou até mesmo não possuírem essas informações cadastradas no banco de dados da Rede NetImóveis. A resposta a seguir revela que 7.634 ou aproximadamente 36% dos dados está em uma dessas situações mencionadas anteriormente, um valor bem expressivo.
\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7634"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de imóveis que são isentos de IPTU ou não possuem taxa de condomínio\n",
"len(train.loc[(train['Valor_IPTU']==0) | (train['Valor_Cond']==0)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
A razão da baixa correlação entre os preços dos imóveis e as coordenadas geográficas dos mesmos se deve ao fato da latitude e longitude não possuirem nenhuma propiedade ordinária, como a idade de uma pessoa. Por exemplo, os dois pontos (-19.9385153, -43.9558656) e (-19.9285010, -43.9558656) não possuem nenhum significado ou ordem de grandeza. Eles são apenas dois pontos no espaço como qualquer outro. Desde modo, a latitude e a longitude não podem ser aplicadas diretamente nos modelos de predição.
\n",
"\n",
">
Uma alternativa é utilizar um algoritmo de clausterização para criar zonas (cluster) de dados que estão geograficamente próximos. Ir-se-á utilizar o algoritmo K-means.
Ao se analisar a distribuição geográfica dos dados do conjunto de treino, observa-se várias zonas de clausterização e também alguns outliers (dados com informações geográficas não consistente com a região classificada). Para se determinar o número ideal de clusters na distribuição dos dados, pode-se utilizar o método do cotovelo (Elbow Method) que consiste em encontrar visualmente o ponto de maior inflexão da curva números de clusters vs. erro do proceso de clausterização.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plota a distribuição geográfica dos dados\n",
"\n",
"def Remove_Bright_Colors(colors):\n",
" bright_colors = ['white','snow','w','whitesmoke', 'blanchedalmond','ghostwhite',\n",
" 'azure','aliceblue','mintcream','ivory','floralwhite','oldlace',\n",
" 'cornsilk','papayawhip','lightgoldenrodyellow','antiquewhite',\n",
" 'lavenderblush','linen','lemonchiffon','honeydew','mistyrose',\n",
" 'lightgray','lightgrey','peachpuff']\n",
" \n",
" for color in bright_colors:\n",
" colors.remove(color)\n",
" \n",
" return colors\n",
" \n",
" \n",
"def PlotLocations(df,clusters = False, title = None):\n",
" # Sepera todas as regiões\n",
" regions = df['Regiao'].loc[~df['Regiao'].isnull()].unique()\n",
" # Lista contendo os dados seperados por região\n",
" data = []\n",
" # Loop para separar os dados por região\n",
" for region in regions:\n",
" data.append((df['Latitude'].loc[df['Regiao']==region] ,\n",
" df['Longitude'].loc[df['Regiao']==region]))\n",
" \n",
" # Cores que aperação no gráfico\n",
" colors = ('gray','green','brown','blue','black','purple','olive','gold','red')\n",
" \n",
" # Cores que aperação no gráfico de clusters\n",
" if clusters:\n",
" colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\n",
" colors = Remove_Bright_Colors(colors)\n",
" random.shuffle(colors)\n",
" colors = colors\n",
" \n",
" # Dimensona o tamanho da figura\n",
" fig = plt.figure(figsize=(8*1.5, 6*1.5))\n",
" ax = fig.add_subplot(1, 1, 1, facecolor=\"1.0\")\n",
" \n",
" # Posiciona o pontos\n",
" for data, color, region in zip(data, colors, regions):\n",
" x, y = data\n",
" ax.scatter(x,y, alpha=0.8, c=color, edgecolors='none', s=30, label=region)\n",
" \n",
" # Configura o gráfico\n",
" plt.title(title)\n",
" #----------------------------\n",
" x_lower = df['Latitude'].min()\n",
" x_upper = df['Latitude'].max()\n",
" y_lower = df['Longitude'].min()\n",
" y_upper = df['Longitude'].max()\n",
" x_resolution = abs(x_upper-x_lower)/len(df)\n",
" y_resolution = abs(y_upper-y_lower)/len(df)\n",
" x_lower = x_lower - 100*x_resolution\n",
" x_upper = x_upper + 100*x_resolution\n",
" y_lower = y_lower - 100*y_resolution\n",
" y_upper = y_upper + 100*y_resolution\n",
" plt.xlim(x_lower,x_upper,x_resolution)\n",
" plt.ylim(y_upper,y_lower,y_resolution)\n",
" #-------------------------------------\n",
" plt.xlabel('Latitude', fontsize=12)\n",
" plt.ylabel('Longitude', fontsize=12)\n",
" if not clusters: plt.legend(loc='lower left')\n",
" plt.grid(True)\n",
" plt.show()\n",
" \n",
"# Visualizando os dados:\n",
"PlotLocations(train,clusters=False,title ='Distribuição Geográfica dos Dados por Região')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
Contudo, sabe-se que dividir a distribuição de preços dos imóveis no território da cidade nas noves regiões mostradas é uma aproximação bastante grosseira. Vamos, portanto, definir uma quantidade de centroides tal que permita cada cluster ter no mínimo 0,5% dos dados (82 amostras) do conjunto de treino. Após uma série de tentativas, verificou-se que 60 centroides é a quantidade limiar da regra estabelecida. Acima desse valor, os dados ficam melhores subdivididos no espaço mas algumas regiões apresentam uma quantidade ínfima de amostras.
"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
" _nan_object_mask = _nan_object_array != _nan_object_array\n"
]
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import sklearn\n",
"from sklearn.cluster import KMeans\n",
"# Encontra a distribuição de clusters que atende a regra estabelicida (cada cluster deve ter no mínimo 82 dados)\n",
"while True:\n",
" # Define o número de centroides\n",
" clusters = KMeans(50)\n",
" # Determina os clusters\n",
" clus = clusters.fit_predict(train[['Latitude', 'Longitude']])\n",
" centros = np.unique(clus)\n",
" clus = list(clus)\n",
" # Verifica se a regra foi atendida\n",
" data = []\n",
" for centro in centros:\n",
" if clus.count(centro) < 107:\n",
" data.append((clus.count(centro)))\n",
" \n",
" if len(data)==0:\n",
" break\n",
"\n",
"# Define o número de centroides\n",
"clusters = KMeans(60)\n",
"# Determina os clusters\n",
"clus = clusters.fit_predict(train[['Latitude', 'Longitude']])\n",
"centros = np.unique(clus)\n",
"clus = list(clus)\n",
" \n",
" \n",
"# Organiza os clusters e os dados\n",
"clusters_df = pd.DataFrame({\"ImovelID\":train['ImovelID'],\"Latitude\":train['Latitude'], \n",
" \"Longitude\":train['Longitude'],\"Regiao\":clus})\n",
"# Exibe o gráfico:\n",
"PlotLocations(clusters_df,clusters=True,\n",
" title = 'Distribuição Geográfica dos Dados por Zonas Clausterizadas')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
O resultado mostrado na imagem anterior indica que a aproximação da distribuição geográfica dos preços por 50 zonas distintas é razoável. Ao se computar a média e o desvio padrão de cada umas regiões mostradas na Figura 22,\n",
"cria-se mais duas variáveis, Mean_PreçoZone e Std_PreçoZone, no conjunto de dados. A\n",
"matriz de correlação mostrada a seguir revela que essas duas novas variáveis possuem uma\n",
"relação significativa com os preços e ajudam agregar valor quantitativo à localidade dos imóveis.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def transform_prices (train, clusters):\n",
" # Determina o cluster de cada amostra\n",
" clus = clusters.predict(train[['Latitude', 'Longitude']])\n",
"\n",
" # Associa os clusters às colunas ImovelID e Preço\n",
" zonas = pd.DataFrame({\"ImovelID\":train['ImovelID'],\"Zona\":clus,\"Preço\":train['Preço']})\n",
"\n",
" # Cria um Dataframe Zonas_Preço que associa a média e o desvio padrão de cada cluster \n",
" centros = np.unique(clus)\n",
" zonas_preço = pd.DataFrame({\"Zona\":centros})\n",
" zonas_preço['Mean_PreçoZone'] = 0\n",
" zonas_preço['Std_PreçoZone'] = 0\n",
"\n",
" # Computa a média e o desvio padrão dos preços de cada zona\n",
" for centro in centros:\n",
" preço_metro = zonas.loc[zonas['Zona']==centro]['Preço']\n",
" zonas_preço.iloc[zonas_preço.loc[zonas_preço['Zona']==centro].index,1] = preço_metro.mean()\n",
" zonas_preço.iloc[zonas_preço.loc[zonas_preço['Zona']==centro].index,2] = preço_metro.std()\n",
"\n",
" # Insere no dataframe train a média e o desvio padrão de cada zona\n",
" train = pd.merge(train,\n",
" pd.merge(zonas,zonas_preço,on=\"Zona\",how='left')[[\"ImovelID\",'Zona',\n",
" 'Mean_PreçoZone','Std_PreçoZone']],\n",
" on=\"ImovelID\", how= 'inner')\n",
"\n",
" columns = ['ImovelID','Preço','Area','Qtde_Quartos','Qtde_Banheiros','Qtde_Suites',\n",
" 'Vagas_Garagem','Valor_Cond','Valor_IPTU','Latitude','Longitude','Mean_PreçoZone',\n",
" 'Std_PreçoZone']\n",
"\n",
" train = train[columns]\n",
" \n",
" return train, zonas_preço\n",
"\n",
"train, zonas_preço = transform_prices (train.iloc[:,0:11], clusters)\n",
"# Mapa de coorelação entre as variáveis do problema:\n",
"corrmat = train.iloc[:,1:13].corr()\n",
"plt.subplots(figsize=(12,9))\n",
"sns.heatmap(corrmat, vmax=0.9, annot=True, square=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
Finalmente, o mapa de correlação mostrado acima indica que a transformação de variáveis realizada enriqueceu bastante as informações do problema. Portanto, as características que serão utilizadas para modelar os preços dos apartamentos são as seguintes: \n",
"\n",
"
A distribuição normal, ou distribuição gaussiana, é a distribuição preferida dos estatísticos e dos cientistas de dados por serem capazes de modelar uma vasta quantidade de fenômenos naturais e por tornarem as operações matemáticas de análise relativamente simples, se comparadas à de outras distribuições. Ademais, o teorema central do limite 9 afirma que quando se aumenta a quantidade de amostras de um evento, a distribuição amostral da sua média aproxima-se cada vez mais de uma distribuição normal. Como foi visto no Capítulo 3, os algoritmos de aprendizado de máquina são fundamentalmente baseados em modelos matemáticos; portanto,\n",
"esses algoritmos tendem a performar melhor quando os dados estão distribuídos conforme uma\n",
"distribuição normal.
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gerando o SeaBorn gráfico que mostra a distribuição dos preços\n",
"def IsDataNormDistribued(df):\n",
" \n",
" # Gera o Seaborn gráfico que mostra a distrubição dos dados\n",
" a4_dims = (8*1.5, 6*1.5)\n",
" fig, ax = pyplot.subplots(figsize=a4_dims)\n",
" fig.set_size_inches(8*1.5, 6*1.5)\n",
" sns.distplot(df['Preço'] , fit=norm, ax=ax);\n",
"\n",
" # Calcula a distribuição normal que mais se aproxima dos daos\n",
" (mu, sigma) = norm.fit(df['Preço'])\n",
" print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
"\n",
" # Plota o gráfico de distribuição\n",
" plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
" loc='best')\n",
" plt.ylabel('Frequency')\n",
" plt.title('Distribuição dos Preços')\n",
"\n",
" # Plota o gráfico de probabilidades\n",
" fig = plt.figure(figsize=(8*1.5, 6*1.5))\n",
" y_lower = Define_Axis_Limits(df['Preço'].min(),False)\n",
" Define_Axis_Limits(90,False)\n",
" res = stats.probplot(df['Preço'], plot=plt)\n",
" plt.title('Gráfico de Probabilidades')\n",
" plt.show()\n",
"\n",
"IsDataNormDistribued(train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
O gráficos acima sugerem que a distribuição dos preços não segue uma distribuição normal, algo que não é desejável. Realizar-se-á uma transformação logarítma (log(1+x)) nos preços dos apartamentos na esperança de tornar a distribuição desses dados mais próxima de uma distribuição \"normal\".
Uma outra análise a ser realizada é com relação a assimetria das distribuições das variáveis independentes. A assimetria (em inglês: skewness) mede o quanto a cauda lado esquerdo de uma distribuição é maior do que a cauda do lado direito ou vice-versa. Uma assimetria positiva indica que a cauda do lado direito é maior que a do lado esquerdo e uma assimetria negativa, obviamente, indica o contrário.
\n",
"\n",
">
A bibloteca pandas oferece uma função chamada \"skew\", viés em português, que determina o quão assimétrica a distribuição de um conjunto de dados está em relação à sua distribuição normal. Neste link há mais detalhes sobre o que essa função faz. Ir-se-á, portanto, utilizar a função skew para identificar variáveis no conjunto de treino que apresentam uma assimetria elevada.
"
],
"text/plain": [
" Assimetria\n",
"Valor_Cond 143.6802\n",
"Valor_IPTU 81.6895\n",
"Vagas_Garagem 3.0675\n",
"Area 2.1436\n",
"Qtde_Banheiros 2.1179\n",
"Mean_PreçoZone 1.4953\n",
"Std_PreçoZone 1.3009\n",
"Qtde_Suites 1.1771\n",
"Latitude 0.4521\n",
"Qtde_Quartos 0.0391\n",
"Longitude -0.2153"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantidade de variáveis do conjunto de treino com distribuição assimétrica\n",
"numeric_feats = train.iloc[:,2:14].dtypes[train.iloc[:,2:14].dtypes != \"object\"].index\n",
"# Verificando a assimetria das variaveis do problemaa\n",
"skewed_feats = train.iloc[:,2:14][numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
"skewness_train = pd.DataFrame({'Assimetria' :skewed_feats})\n",
"# Variaveis com distribuição assimétrica no conjunto de treino\n",
"skewness_train.head(11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
TRANSFORMAÇÃO BOX COX NAS VARIÁVEIS ALTAMENTE ENVIESADAS
\n",
"\n",
">
Este artigo explica resumidamente no que se consiste a transformação Box Cox. Vamos utilizar a função \"boxcox1p\" que computa a tranformação Box Cox de 1 + x. Oberseve que ao definir lambda=0 é o mesmo que usar a tranformação logaratma feita na variável preço anteriormente.
"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"from scipy.special import boxcox1p\n",
"# Seleciona as variáveis que possuem coefieciente de assimétria maior do 1\n",
"skewness = skewness_train[abs(skewness_train.Assimetria)>1]\n",
"# Realiza a tranformação Box Cox utilizando lambda = 0.5\n",
"skewed_features = skewness.index\n",
"lam = 0.5\n",
"for feat in skewed_features:\n",
" #all_data[feat] += 1\n",
" train[feat] = boxcox1p(train[feat], lam)\n",
"\n",
"numeric_feats = train.iloc[:,2:14].dtypes[train.iloc[:,2:14].dtypes != \"object\"].index\n",
"# Verificando a assimetria das variaveis do problema\n",
"skewed_feats = train.iloc[:,2:14][numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
"skewness_train = pd.DataFrame({'Assimetria' :skewed_feats})\n",
"# Variaveis com distribuição assimétrica no conjunto de treino\n",
"skewness_train.head(11) \n",
"\n",
"train.to_csv(\"/home/gpassos/Documents/tcc/data/train_transformed.csv\", encoding=\"utf-8\", sep=\",\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
4. MODELAGEM E PREDIÇÃO DOS PREÇOS DO CONJUNTO DE TESTE
\n",
"\n",
">
Nesta etapa, avaliar-se-á a perfomance dos principais modelos de regressão da biblioteca scikit-learn na predição dos preços do conjunto de teste.
O principal objetivo de se utilizar algoritmos de aprendizado de máquina em problemas da vida real é obter um modelo computacional que seja capaz de produzir respostas eficazes para situações novas de um mesmo problema. Em outras palavras, a relevância dos resultados dos modelos está atrelada ao desempenho dos mesmos na predição das amostras do conjunto de teste, pois não faz sentido prático predizer os resultados das amostras de treino. Assim, faz-se necessário definir uma estratégia de validação da predição dos preços das 21.232 amostras.
\n",
"\n",
">
Uma maneira de se resolver tal problema é usar a técnica denominada k-fold Cross Validation (JAMES et al., 2014), popularmente conhecida entre os cientistas de dados. Basicamente, essa técnica consiste em dividir as amostras aleatoriamente em k grupos, ou dobras (em inglês: folds), de tamanho aproximado. Então, o primeiro grupo k1 de amostras é usado como conjunto de validação e os k − 1 grupos restantes são usados como conjunto de treino. O erro quadrático médio (EQM) (em inglês: mean squared error ou MSE) do experimento é computado e armazenado. Esse processo é repetido k vezes e, em cada vez, um grupo diferente de amostras ki é tratado como conjunto de validação e os restantes ki − 1 grupos são usados como conjunto de treino. Esse procedimento produz então k estimativas de erros de testes, MSE1, MSE2 , . . . ,MSEk . Desse modo, a estimativa de erro do procedimento k-fold Cross Validation é definida pela média dos MSE k erros computados em cada experimento
"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Função para associação da média e do desvio padrão dos preços ao dados de teste\n",
"def transform_test_set(test,clusters,zonas_preço):\n",
" \n",
" clus = clusters.predict(test[['Latitude', 'Longitude']])\n",
"\n",
" # Concatena a média e o desvio padrão do preços no conjunto de treino\n",
" zonas = pd.DataFrame({\"ImovelID\":test['ImovelID'],\"Zona\":clus})\n",
" test = pd.merge(test,\n",
" pd.merge(zonas,zonas_preço,on=\"Zona\",how='left')[[\"ImovelID\",'Mean_PreçoZone',\n",
" 'Std_PreçoZone','Zona']],\n",
" on=\"ImovelID\", how= 'inner')\n",
"\n",
" columns = ['ImovelID','Preço','Area','Qtde_Quartos','Qtde_Banheiros','Qtde_Suites',\n",
" 'Vagas_Garagem','Valor_Cond','Valor_IPTU','Latitude','Longitude','Mean_PreçoZone',\n",
" 'Std_PreçoZone']\n",
"\n",
" test=test[columns]\n",
" \n",
" return test\n",
"\n",
"\n",
"# Função de validação cruzada\n",
"def cross_validation_score(estimator,X,clusters,n_folds=5): \n",
" # Shuffling o conjunto de dados\n",
" X = X.sample(frac=1).reset_index(drop=True) \n",
" kf = KFold(n_splits = n_folds, shuffle = True, random_state = 2)\n",
" # Coluna auxiliares\n",
" X['Real_PreçoT'] = 0\n",
" X['Pred_Preço'] = 0\n",
" # Listas auciliares\n",
" MAPE = []\n",
" less_10per = []\n",
" less_20per = []\n",
" time_lst = []\n",
" # Filtros de colunas\n",
" column = X.columns.get_loc('Pred_Preço')\n",
" columns = [x for x in X.columns if x not in ['Preço','ImovelID','Real_PreçoT','Pred_Preço']]\n",
" columns.append('Mean_PreçoZone')\n",
" columns.append('Std_PreçoZone')\n",
" \n",
" for train_index, test_index in kf.split(X):\n",
" # inicia a contagem de tempo\n",
" start = time.time()\n",
" # Calcula a média e o desvio padrão dos preços dos dados treino\n",
" train_set, zonas_preço = transform_prices(X.iloc[train_index],clusters)\n",
" # Treina o modelo\n",
" estimator.fit(train_set.loc[:,columns],train_set.Preço)\n",
" \n",
" # Associa a média e os preços dos dados de treino ao conjunto de teste\n",
" test_set = transform_test_set(X.iloc[test_index],clusters,zonas_preço)\n",
" \n",
" # Prediz o preços dos dados de treino\n",
" X.iloc[test_index,column]=np.expm1(estimator.predict(test_set.loc[:,columns]))\n",
" # Encerra contagem\n",
" end = time.time()\n",
" \n",
" APE = abs(np.expm1(X.loc[test_index].Preço)- \n",
" X.loc[test_index].Pred_Preço)/np.expm1(X.loc[test_index].Preço)\n",
" MAPE.append(APE.mean())\n",
" less_10per.append(len(APE.loc[APE<0.1])/len(APE))\n",
" less_20per.append(len(APE.loc[APE<0.2])/len(APE)) \n",
" time_lst.append(end-start)\n",
" \n",
" # Computa o Erro Absoluto Percentual\n",
" X['APE'] = abs(np.expm1(X.Preço)- \n",
" X.Pred_Preço)*100/np.expm1(X.Preço)\n",
" \n",
" # Preços reais\n",
" X['Real_PreçoT'] = np.expm1(X['Preço'])\n",
" \n",
" # Calcula o Erro Médio Absoluto Perceutal\n",
" MAPE = np.array(MAPE).mean()*100\n",
" less_10per = np.array(less_10per).mean()*100\n",
" less_20per = np.array(less_20per).mean()*100\n",
" time_ = np.array(time_lst).mean()\n",
" m, s = divmod(time_, 60)\n",
" \n",
" columns = ['MAPE','Dentro de ± 10%','Dentro de ± 20%', 'Tempo Médio Pred.']\n",
"\n",
" # Resposta\n",
" return X,pd.DataFrame({'MAPE':'{:.3f}'.format(MAPE) + \"%\",\n",
" 'Dentro de ± 10%':'{:.3f}'.format(less_10per) + \"%\",\n",
" 'Dentro de ± 20%':'{:.3f}'.format(less_20per) + \"%\",\n",
" 'Tempo Médio Pred.':\"%02dmin %02dsec\" %(m, s)}, index=[0])[columns]\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">
4.1 PRINCIPAIS MODELOS BASE DA BIBLIOTECA DO SCKIT-LEARN
Definiu-se o número de camadas escondidas igual a 1000 e a função a unidade linear retificada como função de ativação dos neurônios.\n",
"Ademais, definiu-se o método de otimização estocástico gradient-based para ajustes dos pesos w dos neurônios.