diff --git a/Aula01_Atividade_Manipular_Filtrar_DF.ipynb b/Aula01_Atividade_Manipular_Filtrar_DF.ipynb index 36dc5d9..6652133 100644 --- a/Aula01_Atividade_Manipular_Filtrar_DF.ipynb +++ b/Aula01_Atividade_Manipular_Filtrar_DF.ipynb @@ -4,6 +4,64 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "___\n", "# Atividade:

Manipulação de um `DataFrame` com uso do **Pandas**\n", "___\n", @@ -17,6 +75,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
\n", "\n", "## Índice\n", diff --git a/Aula01_Exercicio__Manipular_Filtrar_DF.ipynb b/Aula01_Exercicio__Manipular_Filtrar_DF.ipynb index 05636cf..9aeea74 100644 --- a/Aula01_Exercicio__Manipular_Filtrar_DF.ipynb +++ b/Aula01_Exercicio__Manipular_Filtrar_DF.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ "output_type": "stream", "text": [ "Esperamos trabalhar no diretório: \n", - "c:\\Users\\ribei\\OneDrive\\Área de Trabalho\\cdados\\cdados\n", + "c:\\Users\\PH\\Desktop\\Repositorios\\cdados\n", "\n" ] } @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -271,7 +271,7 @@ "4 20619075.0 1.083223e+11 Americas South America " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -326,7 +326,7 @@ "dtype: object" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -337,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -479,7 +479,7 @@ "[185 rows x 5 columns]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -522,13 +522,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "10209 China\n", + "10251 India\n", + "10350 United States\n", + "10252 Indonesia\n", + "10197 Brazil\n", + "Name: country, dtype: object\n", "['China', 'India', 'United States', 'Indonesia', 'Brazil']\n" ] }, @@ -596,7 +602,7 @@ "175 United States 36.486084" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -604,6 +610,7 @@ "source": [ "# Coloque seu código aqui...\n", "lista = gap.loc[gap['year'] == 2015, :].sort_values(by='population', ascending=False).head(5).loc[:, 'country']\n", + "print(lista)\n", "lista = list(lista)\n", "print(lista)\n", "\n", @@ -652,7 +659,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Aula02_Exercicio_TeoriaDaProbabilidade.ipynb b/Aula02_Exercicio_TeoriaDaProbabilidade.ipynb index f13a258..44c0e61 100644 --- a/Aula02_Exercicio_TeoriaDaProbabilidade.ipynb +++ b/Aula02_Exercicio_TeoriaDaProbabilidade.ipynb @@ -926,7 +926,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Aula04_Atividade_Titanic_ClassificadorBayes.ipynb b/Aula04_Atividade_Titanic_ClassificadorBayes.ipynb index b466fdc..7a17e0d 100644 --- a/Aula04_Atividade_Titanic_ClassificadorBayes.ipynb +++ b/Aula04_Atividade_Titanic_ClassificadorBayes.ipynb @@ -3286,7 +3286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Aula17_Exemplo_Chatbot.ipynb b/Aula17_Exemplo_Chatbot.ipynb new file mode 100644 index 0000000..c6f68dd --- /dev/null +++ b/Aula17_Exemplo_Chatbot.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "# Exemplo:

Distribuição Poisson\n", + "\n", + "___\n", + "## Aula 17" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "## Lista de comandos:\n", + "\n", + "Comandos quando $X\\sim Poisson(\\mu)$\n", + "\n", + "- $P(X=k)$: `stats.poisson.pmf(k,mu)`\n", + "- $P(X\\leq k)$: `stats.poisson.cdf(k, mu)`\n", + "- $E(X)$: `stats.poisson.mean(mu)`\n", + "- $Var(X)$: `stats.poisson.var(mu)`\n", + "- $DP(X)$: `stats.poisson.std(mu)`\n", + "\n", + "Link: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O número de conversas que se iniciam entre usuário e chatbot em um site e-commerce ocorre a uma taxa média de 4 a cada minuto.\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "mu : valor médio por minuto\n", + "mu = 4\n", + "\n", + "X: numero de conversas entre usuario e chatbot por minuto\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "a) Calcule a probabilidade de ocorrer 3 conversas entre usuário e chatbot em um minuto qualquer do dia?\n", + "\n", + "*Resposta esperada: 0.19536681481316454*" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.19536681481316454" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui\n", + "mu = 4\n", + "stats.poisson.pmf(3, mu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "b) Calcule a probabilidade de ocorrer **menos de 3** conversas entre usuário e chatbot em um minuto qualquer do dia.\n", + "\n", + "*Resposta esperada: 0.23810330555354436*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.23810330555354436" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui\n", + "stats.poisson.cdf(2, mu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "c) Calcule a probabilidade de ocorrer **menos de 4** conversas entre usuário e chatbot em um minuto qualquer do dia, **sabendo que houve pelo menos 1 conversa entre usuário e chatbot**.\n", + "\n", + "*Resposta esperada: 0.42290016824554516*" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.422900168245545" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui\n", + "(stats.poisson.cdf(4, mu) - stats.poisson.cdf(1, mu) - stats.poisson.pmf(4, mu) + stats.poisson.pmf(1, mu)) / (1 - stats.poisson.cdf(1, mu) + stats.poisson.pmf(1, mu))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**EXTRA:** Pergunta a cada 2 minutos:\n", + "Calcule a probabilidade de ocorrem exatamente 3 inícios de conversa a cada **dois minutos** quaisquer do dia? \n", + "\n", + "*Resposta esperada: 0.028626144247680996*" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.028626144247680996" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui\n", + "stats.poisson.pmf(3, 8)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Aula17_Exercicio_APS_CombinacaoDeVariaveisAleatorias.ipynb b/Aula17_Exercicio_APS_CombinacaoDeVariaveisAleatorias.ipynb new file mode 100644 index 0000000..15b42ca --- /dev/null +++ b/Aula17_Exercicio_APS_CombinacaoDeVariaveisAleatorias.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "# Exercício para APS:

Propriedades de esperança e variância

para Combinação linear de variáveis aleatórias\n", + "___\n", + "\n", + "## Aula 17" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "## Índice\n", + "\n", + "- [Exercício 1](#ex1) **- APS9 disponível no Blackboard**\n", + "- [Exercício 2](#ex2) **- APS9 disponível no Blackboard**\n", + "- [Exercício 3](#ex3) **- APS9 disponível no Blackboard**\n", + "- [Exercício 4](#ex4) **- APS9 disponível no Blackboard**\n", + "- [Exercício 5](#ex5) **- APS9 disponível no Blackboard**\n", + "\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from scipy.stats import uniform, expon, norm\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "### EXERCÍCIO 1\n", + "\n", + "Um processo industrial pode ser executado em duas etapas independentes. O tempo gasto em cada etapa leva, em média, 5 horas com desvio padrão de 10 horas.\n", + "\n", + "Um engenheiro resolveu modificar o processo de produção fazendo com que seja executado numa única fase, cujo novo tempo de execução agora é o dobro da primeira etapa do processo atual.\n", + "\n", + "O novo processo proposto pelo engenheiro deve ser adotado?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# ESCREVA SEU CÓDIGO AQUI\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "### ENUNCIADO PARA EXERCÍCIOS 2 e 3\n", + "\n", + "### **Quanto custa em média?**\n", + "\n", + "O fluido de freio custa, em média, 50 reais. As pastilhas possuem variação alta: em um carro popular custa 80 reais o par, mas pode chegar a 500 reais em um importado. O par das lonas de freio custa 80 reais, o de tambor 180 reais e o de disco 250 reais.\" Extraído da [Revista Auto Esporte](https://revistaautoesporte.globo.com/Servico/autoajuda/noticia/2015/05/autoajuda-freios.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### EXERCÍCIO 2\n", + "\n", + "Com a reportagem acima, vimos que as pastilhas de freio têm preços variados, a depender de diversos fatores como marca do carro.\n", + "\n", + "Em uma determinada loja de autopeças, o preço médio das pastilhas de freio é de 112 reais, com um desvio-padrão associado a essa variável de 22 reais. \n", + "\n", + "Um pessoa entra nessa loja e deseja comprar três pastilhas de freio, sendo todas iguais.\n", + "\n", + "Qual o gasto total esperado dessa pessoa e respectivo desvio padrão?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "336" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ESCREVA SEU CÓDIGO AQUI\n", + "112*3" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "22*3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "### EXERCÍCIO 3\n", + "\n", + "Com a reportagem acima, vimos que as pastilhas de freio têm preços variados, a depender de diversos fatores como marca do carro.\n", + "\n", + "Em uma determinada loja de autopeças, o preço médio das pastilhas de freio é de 112 reais, com um desvio-padrão associado a essa variável de 22 reais. \n", + "\n", + "Uma outra pessoa entra nessa loja e deseja comprar três pastilhas de freio, porém fará escolhas indenpendentes e não necessariamente iguais.\n", + "Qual o gasto total esperado dessa outra pessoa e respectivo desvio padrão?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "336" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ESCREVA SEU CÓDIGO AQUI\n", + "112*3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "38.1051177665153" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sqrt(22**2 + 22**2 + 22**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "### EXERCÍCIO 4\n", + "\n", + "Joãozinho sai de casa para comprar um notebook e um celular novos!\n", + "\n", + "O notebook tem preço médio de R$\\rm{$}$ 2.500,00, com desvio padrão igual a R$\\rm{\\$}$ 1.000,00.\n", + "\n", + "O celular tem preço médio de R$\\rm{$}$ 1.300,00, com desvio padrão igual a R$\\rm{$}$ 700,00.\n", + "\n", + "Assuma que os preços desses produtos sejam independentes.\n", + "\n", + "Joãozinho sabe que terá um gasto alto e, por isso, irá de ônibus. Com isso, deverá acrescentar mais R$\\rm{$}$40,00 de gasto.\n", + "\n", + "Qual o gasto esperado e respectivo desvio padrão de Joãozinho?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3840" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ESCREVA SEU CÓDIGO AQUI\n", + "2500+1300+40" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1220.66" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(np.sqrt(1000**2 + 700**2),2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "### EXERCÍCIO 5\n", + "\n", + "Aninha é mãe de bebezinhos gêmeos.\n", + "\n", + "Ela sairá de casa para comprar uma roupa e um brinquedo para cada um.\n", + "\n", + "O preço médio da roupa é de R$\\rm{$}$ 60,00, com desvio padrão igual a R$\\rm{$}$ 15,00.\n", + "\n", + "O preço médio do brinquedo é de R$\\rm{$}$ 75,00, com desvio padrão igual a R$\\rm{$}$ 25,00.\n", + "\n", + "Assuma que esses produtos tenham preços variando de forma independente entre si.\n", + "\n", + "\n", + "Aninha deseja comprar roupas iguais para os gêmeos, mas os brinquedos não necessariamente serão os mesmos.\n", + "\n", + "Nesse caso, qual o gasto esperado de Aninha e o respectivo desvio padrão?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "270" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ESCREVA SEU CÓDIGO AQUI\n", + "60*2 + 75*2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "65.36" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(2*15 + np.sqrt(25**2 + 25**2), 2)" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Aula17_Exercicio_ModelosProbabil\303\255sticosDiscretos.ipynb" "b/Aula17_Exercicio_ModelosProbabil\303\255sticosDiscretos.ipynb" new file mode 100644 index 0000000..9d952cb --- /dev/null +++ "b/Aula17_Exercicio_ModelosProbabil\303\255sticosDiscretos.ipynb" @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "# Exercício:

Modelos probabilísticos discretos\n", + "___\n", + "\n", + "## Aula 17" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "## Índice\n", + "\n", + " - [Exercício 1](#ex1) **- APS8 disponível no Blackboard**\n", + " - [Exercício 2](#ex2)\n", + " - [Exercício 3](#ex3)\n", + " - [Exercício 4](#ex4)\n", + "\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats #importa apenas as funções de estatísticas da biblioteca SciPy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### EXERCÍCIO 1\n", + "\n", + "Um pendrive permite 60 minutos de gravação de um vídeo em qualidade full HD. Sabe-se que defeitos de fabricação surgem a uma taxa de 1,2 defeitos a cada 10 horas de gravação. \n", + "\n", + "**Preencha as respostas dos itens a, b, c na APS8 (via Blackboard).**\n", + "\n", + "a) Qual é a probabilidade de um pendrive apresentar defeito de fabricação? \n", + "\n", + "b) Sabendo que um pendrive apresentou defeito, qual a probabilidade de que ele tenha no máximo dois defeitos? \n", + "\n", + "c) Uma pessoa compra uma embalagem com 10 pendrives. Qual é a probabilidade de pelo menos 3 apresentarem defeito de fabricação? \n", + "\n", + "d) Quais suposições você fez para responder os itens (a) e (c)?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.11307956328284244" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ESCREVA SUA RESPOSTA AQUI\n", + "mu = 1.2 # defeitos / 10h\n", + "1 - stats.poisson.cdf(1, mu/10) + stats.poisson.pmf(1, mu/10)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.99767169482463" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1 - stats.poisson.cdf(1, mu/10) + stats.poisson.pmf(1, mu/10) - (1 - stats.poisson.cdf(2, mu/10))) / (1 - stats.poisson.cdf(1, mu/10) + stats.poisson.pmf(1, mu/10))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.09447055387581543" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 - stats.binom.cdf(3, 10, (1 - stats.poisson.cdf(1, mu/10) + stats.poisson.pmf(1, mu/10))) + stats.binom.pmf(3, 10, (1 - stats.poisson.cdf(1, mu/10) + stats.poisson.pmf(1, mu/10)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### EXERCÍCIO 2\n", + "\n", + "Uma das responsabilidades da área de Tecnologia da Informação (TI) de uma empresa é controlar o fluxo de mensagens eletrônicas e detectar a presença de SPAMs (mensagens não desejadas). \n", + "Os responsáveis pela área de TI de uma empresa detectaram que um funcionário recebe no seu e-mail individual da empresa, em média, 0,75 SPAM por dia. Assuma que os SPAMs são enviados de forma independente para cada funcionário da empresa. \n", + "\n", + "a) Qual a probabilidade de um funcionário receber SPAM em um dia?\n", + "\n", + "b) Sabendo que um funcionário recebeu SPAM em uma semana, qual a probabilidade de receber entre 10 e 15 SPAMs nessa semana?\n", + "\n", + "c) Selecionando 40 funcionários aleatoriamente (e considerando que os funcionários recebem ou não SPAMs de forma independente um do outro), qual é a probabilidade de vinte deles receberem SPAM no seu e-mail individual em um dia qualquer? \n", + "\n", + "*Respostas esperadas respectivamente: 0.5276334472589853 ; 0.0419265935825364 ; 0.11793018200429256*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# ESCREVA SUA RESPOSTA AQUI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### EXERCÍCIO 3\n", + "\n", + "Uma rede congestionada de computadores tem 1% de probabilidade de perder um bloco de dados, e perdas de blocos são eventos independentes. Uma mensagem de e-mail requer 100 blocos de dados. \n", + "\n", + "a) Qual a distribuição do número de blocos de dados que devem ser reenviados em uma mensagem de e-mail? Inclua os valores dos parâmetros.\n", + "\n", + "b) Qual é a probabilidade de no mínimo um bloco ter de ser reenviado?\n", + "\n", + "c) Sabendo que no mínimo um bloco precisa ser reenviado, qual a probabilidade de dois ou mais serem reenviados?\n", + "\n", + "d) Quais são a média e o desvio padrão do número de blocos que devem ser reenviados?\n", + "\n", + "\n", + "*Respostas esperadas (b) a (d) respectivamente: 0.6339676587267705 ; 0.41680047466100517 ; 'Esperança: 1.0 e Desvio Padrão: 0.99498743710662'*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# ESCREVA SUA RESPOSTA AQUI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### EXERCÍCIO 4\n", + "\n", + "O número médio de clientes satisfeitos com o atendimento de uma loja, em amostras de 40 clientes escolhidos ao acaso, é de 5,5. \n", + "\n", + "Qual é a probabilidade de, numa amostra de 40 clientes escolhidos ao acaso, encontrarmos pelo menos 2 satisfeitos?\n", + "\n", + "\n", + "*Respostas esperadas: 0.980128399778501*" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# ESCREVA SUA RESPOSTA AQUI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/CD_Simulado2.ipynb b/CD_Simulado2.ipynb new file mode 100644 index 0000000..ef7e77b --- /dev/null +++ b/CD_Simulado2.ipynb @@ -0,0 +1,1736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "# SIMULADO 2\n", + "\n", + "## 2023 - 2o. semestre \n", + "\n", + "___\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOME: Coloque seu nome AQUI!" + ] + }, + { + "cell_type": "code", + "execution_count": 1295, + "metadata": {}, + "outputs": [], + "source": [ + "# %matplotlib inline\n", + "import math\n", + "from math import sqrt\n", + "\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from numpy import arange\n", + "import pandas as pd\n", + "\n", + "from IPython.display import display # Para ter melhor print.\n", + "\n", + "# Pode trazer mais bibliotecas, caso julgue interessante!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Índice\n", + "\n", + "1. [Pergunta 1](#p1)\n", + "\n", + "1. [Pergunta 2](#p2)\n", + "\n", + "1. [Pergunta 3](#p3)\n", + "\n", + "1. [Pergunta 4](#p4)\n", + "\n", + "1. [Pergunta 5](#p5)\n", + "\n", + "1. [Pergunta 6](#p5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "**Abrindo um arquivo de dados e visualizando seu conteúdo:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1296, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperamos trabalhar no diretório: \n", + "c:\\Users\\PH\\Desktop\\Repositorios\\cdados\n", + "\n" + ] + } + ], + "source": [ + "print(f'Esperamos trabalhar no diretório: \\n{os.getcwd()}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 1297, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parece que o arquivo df_simulado2.xlsx está na mesma pasta do notebook, yay!\n" + ] + } + ], + "source": [ + "filename = 'df_simulado2.xlsx'\n", + "\n", + "if filename in os.listdir():\n", + " print(f'Parece que o arquivo {filename} está na mesma pasta do notebook, yay!')\n", + " \n", + "else:\n", + " print(f'Não encontrei o arquivo {filename}. Lembre-se que precisa estar na mesma pasta do arquivo notebook')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abrindo a base de dados:" + ] + }, + { + "cell_type": "code", + "execution_count": 1298, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "df = pd.read_excel(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Avaliando as 3 primeiras linhas da base de dados:" + ] + }, + { + "cell_type": "code", + "execution_count": 1299, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residenceremote_ratiocompany_locationcompany_sizemonthly_expensies
02020MIFTData Scientist70000EUR79833DE0DEL1382
12020SEFTMachine Learning Scientist260000USD260000JP0JPS1405
22020SEFTBig Data Engineer85000GBP109024GB50GBM1441
\n", + "
" + ], + "text/plain": [ + " work_year experience_level employment_type job_title \\\n", + "0 2020 MI FT Data Scientist \n", + "1 2020 SE FT Machine Learning Scientist \n", + "2 2020 SE FT Big Data Engineer \n", + "\n", + " salary salary_currency salary_in_usd employee_residence remote_ratio \\\n", + "0 70000 EUR 79833 DE 0 \n", + "1 260000 USD 260000 JP 0 \n", + "2 85000 GBP 109024 GB 50 \n", + "\n", + " company_location company_size monthly_expensies \n", + "0 DE L 1382 \n", + "1 JP S 1405 \n", + "2 GB M 1441 " + ] + }, + "execution_count": 1299, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Avaliando o tipo de cada variável disponível no *dataframe* `df`:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "## PERGUNTA 1: \n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count\n", + "job_title \n", + "Data Scientist 143\n", + "Data Engineer 132\n", + "Data Analyst 97\n", + "Machine Learning Engineer 41\n", + "Research Scientist 16" + ] + }, + "execution_count": 1300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui!\n", + "df.loc[:, ['job_title']].value_counts().to_frame().head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "code", + "execution_count": 1301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Machine Learning Engineer0.067545
Research Scientist0.026359
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" + ], + "text/plain": [ + " proportion\n", + "job_title \n", + "Data Scientist 0.235585\n", + "Data Engineer 0.217463\n", + "Data Analyst 0.159802\n", + "Machine Learning Engineer 0.067545\n", + "Research Scientist 0.026359" + ] + }, + "execution_count": 1301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[:, ['job_title']].value_counts(normalize=True).to_frame().head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "## PERGUNTA 2:\n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1302, + "metadata": {}, + "outputs": [], + "source": [ + "# Coloque seu código aqui!\n", + "df2021_fulltime = df.loc[(df.work_year == 2021) & (df.employment_type == \"FT\"), :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "code", + "execution_count": 1303, + "metadata": {}, + "outputs": [], + "source": [ + "paises = list(df2021_fulltime.loc[:, ['company_location']].value_counts().head(5).keys())\n", + "for i, j in enumerate(paises):\n", + " paises[i] = j[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 1304, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "126644.5021 5679 68604.7854 117789.0 104095.4\n" + ] + } + ], + "source": [ + "salario = df.loc[(df.company_location.isin(paises)) & (df.employment_type == \"FT\"), :].salary_in_usd\n", + "print(round(salario.mean(), 4), round(salario.min(), 4), round(salario.std(), 4), round(salario.median(), 4), round(salario.quantile(q=0.4), 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "
\n", + "\n", + "## PERGUNTA 3:\n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1305, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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remote_ratio050100All
company_size
L0.0527180.0988470.1746290.326194
M0.1301480.0345960.3723230.537068
S0.0263590.0296540.0807250.136738
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" + ], + "text/plain": [ + "remote_ratio 0 50 100 All\n", + "company_size \n", + "L 0.052718 0.098847 0.174629 0.326194\n", + "M 0.130148 0.034596 0.372323 0.537068\n", + "S 0.026359 0.029654 0.080725 0.136738\n", + "All 0.209226 0.163097 0.627677 1.000000" + ] + }, + "execution_count": 1305, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui!\n", + "tabela = pd.crosstab(df.company_size, df.remote_ratio, normalize='all', margins=True)\n", + "tabela" + ] + }, + { + "cell_type": "code", + "execution_count": 1306, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5354" + ] + }, + "execution_count": 1306, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P100dL = tabela[100]['L']/tabela['All']['L']\n", + "round(P100dL, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 1307, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0346" + ] + }, + "execution_count": 1307, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(tabela[50]['M'], 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## PERGUNTA 4:\n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1308, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residenceremote_ratiocompany_locationcompany_sizemonthly_expensies
42020SEFTMachine Learning Engineer150000USD150000US50USL1182
52020ENFTData Analyst72000USD72000US100USL1473
62020SEFTLead Data Scientist190000USD190000US100USS1573
82020MIFTBusiness Data Analyst135000USD135000US100USL1148
132020MIFTLead Data Analyst87000USD87000US100USL1493
.......................................
5992022MIFTData Scientist130000USD130000US100USM1331
6022022SEFTData Engineer154000USD154000US100USM1223
6032022SEFTData Engineer126000USD126000US100USM1201
6042022SEFTData Analyst129000USD129000US0USM1121
6052022SEFTData Analyst150000USD150000US100USM1310
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332 rows × 12 columns

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" + ], + "text/plain": [ + " work_year experience_level employment_type job_title \\\n", + "4 2020 SE FT Machine Learning Engineer \n", + "5 2020 EN FT Data Analyst \n", + "6 2020 SE FT Lead Data Scientist \n", + "8 2020 MI FT Business Data Analyst \n", + "13 2020 MI FT Lead Data Analyst \n", + ".. ... ... ... ... \n", + "599 2022 MI FT Data Scientist \n", + "602 2022 SE FT Data Engineer \n", + "603 2022 SE FT Data Engineer \n", + "604 2022 SE FT Data Analyst \n", + "605 2022 SE FT Data Analyst \n", + "\n", + " salary salary_currency salary_in_usd employee_residence remote_ratio \\\n", + "4 150000 USD 150000 US 50 \n", + "5 72000 USD 72000 US 100 \n", + "6 190000 USD 190000 US 100 \n", + "8 135000 USD 135000 US 100 \n", + "13 87000 USD 87000 US 100 \n", + ".. ... ... ... ... ... \n", + "599 130000 USD 130000 US 100 \n", + "602 154000 USD 154000 US 100 \n", + "603 126000 USD 126000 US 100 \n", + "604 129000 USD 129000 US 0 \n", + "605 150000 USD 150000 US 100 \n", + "\n", + " company_location company_size monthly_expensies \n", + "4 US L 1182 \n", + "5 US L 1473 \n", + "6 US S 1573 \n", + "8 US L 1148 \n", + "13 US L 1493 \n", + ".. ... ... ... \n", + "599 US M 1331 \n", + "602 US M 1223 \n", + "603 US M 1201 \n", + "604 US M 1121 \n", + "605 US M 1310 \n", + "\n", + "[332 rows x 12 columns]" + ] + }, + "execution_count": 1308, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui!\n", + "US = df.loc[df.employee_residence == 'US', :]\n", + "US" + ] + }, + { + "cell_type": "code", + "execution_count": 1309, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.031" + ] + }, + "execution_count": 1309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(US.monthly_expensies.corr(US.salary_in_usd), 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## PERGUNTA 5:\n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1310, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residenceremote_ratiocompany_locationcompany_sizemonthly_expensies
02020MIFTData Scientist70000EUR79833DE0DEL1382
12020SEFTMachine Learning Scientist260000USD260000JP0JPS1405
22020SEFTBig Data Engineer85000GBP109024GB50GBM1441
32020MIFTProduct Data Analyst20000USD20000HN0HNS987
72020MIFTData Scientist11000000HUF35735HU50HUL1498
.......................................
5312022MIFTData Analyst75000USD75000CA0CAM1439
5672022MIFTData Analyst50000GBP65438GB0GBM1204
5862022MIFTData Analyst35000GBP45807GB0GBM1172
6002022ENFTData Analyst67000USD67000CA0CAM1102
6012022ENFTData Analyst52000USD52000CA0CAM1356
\n", + "

252 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " work_year experience_level employment_type job_title \\\n", + "0 2020 MI FT Data Scientist \n", + "1 2020 SE FT Machine Learning Scientist \n", + "2 2020 SE FT Big Data Engineer \n", + "3 2020 MI FT Product Data Analyst \n", + "7 2020 MI FT Data Scientist \n", + ".. ... ... ... ... \n", + "531 2022 MI FT Data Analyst \n", + "567 2022 MI FT Data Analyst \n", + "586 2022 MI FT Data Analyst \n", + "600 2022 EN FT Data Analyst \n", + "601 2022 EN FT Data Analyst \n", + "\n", + " salary salary_currency salary_in_usd employee_residence remote_ratio \\\n", + "0 70000 EUR 79833 DE 0 \n", + "1 260000 USD 260000 JP 0 \n", + "2 85000 GBP 109024 GB 50 \n", + "3 20000 USD 20000 HN 0 \n", + "7 11000000 HUF 35735 HU 50 \n", + ".. ... ... ... ... ... \n", + "531 75000 USD 75000 CA 0 \n", + "567 50000 GBP 65438 GB 0 \n", + "586 35000 GBP 45807 GB 0 \n", + "600 67000 USD 67000 CA 0 \n", + "601 52000 USD 52000 CA 0 \n", + "\n", + " company_location company_size monthly_expensies \n", + "0 DE L 1382 \n", + "1 JP S 1405 \n", + "2 GB M 1441 \n", + "3 HN S 987 \n", + "7 HU L 1498 \n", + ".. ... ... ... \n", + "531 CA M 1439 \n", + "567 GB M 1204 \n", + "586 GB M 1172 \n", + "600 CA M 1102 \n", + "601 CA M 1356 \n", + "\n", + "[252 rows x 12 columns]" + ] + }, + "execution_count": 1310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui!\n", + "nUS = df.loc[df.company_location != 'US', :]\n", + "nUS" + ] + }, + { + "cell_type": "code", + "execution_count": 1311, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7332\\3815165277.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " nUS['salary_in_10kusd'] = nUS.salary_in_usd/10000\n" + ] + } + ], + "source": [ + "nUS['salary_in_10kusd'] = nUS.salary_in_usd/10000" + ] + }, + { + "cell_type": "code", + "execution_count": 1312, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(nUS.salary_in_10kusd, density=True, bins=arange(0, 27, 2), edgecolor=\"black\")\n", + "plt.xticks(arange(0, 27, 2))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## PERGUNTA 6:\n", + "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!" + ] + }, + { + "cell_type": "code", + "execution_count": 1313, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residenceremote_ratiocompany_locationcompany_sizemonthly_expensies
02020MIFTData Scientist70000EUR79833DE0DEL1382
12020SEFTMachine Learning Scientist260000USD260000JP0JPS1405
22020SEFTBig Data Engineer85000GBP109024GB50GBM1441
32020MIFTProduct Data Analyst20000USD20000HN0HNS987
42020SEFTMachine Learning Engineer150000USD150000US50USL1182
.......................................
672020SEFTData Science Manager190200USD190200US100USM1474
682020ENFTData Scientist105000USD105000US100USS1301
692020SEFTData Scientist80000EUR91237AT0ATS1170
702020MIFTData Scientist55000EUR62726FR50LUS1267
712020MIFTData Scientist37000EUR42197FR50FRS1127
\n", + "

72 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " work_year experience_level employment_type job_title \\\n", + "0 2020 MI FT Data Scientist \n", + "1 2020 SE FT Machine Learning Scientist \n", + "2 2020 SE FT Big Data Engineer \n", + "3 2020 MI FT Product Data Analyst \n", + "4 2020 SE FT Machine Learning Engineer \n", + ".. ... ... ... ... \n", + "67 2020 SE FT Data Science Manager \n", + "68 2020 EN FT Data Scientist \n", + "69 2020 SE FT Data Scientist \n", + "70 2020 MI FT Data Scientist \n", + "71 2020 MI FT Data Scientist \n", + "\n", + " salary salary_currency salary_in_usd employee_residence remote_ratio \\\n", + "0 70000 EUR 79833 DE 0 \n", + "1 260000 USD 260000 JP 0 \n", + "2 85000 GBP 109024 GB 50 \n", + "3 20000 USD 20000 HN 0 \n", + "4 150000 USD 150000 US 50 \n", + ".. ... ... ... ... ... \n", + "67 190200 USD 190200 US 100 \n", + "68 105000 USD 105000 US 100 \n", + "69 80000 EUR 91237 AT 0 \n", + "70 55000 EUR 62726 FR 50 \n", + "71 37000 EUR 42197 FR 50 \n", + "\n", + " company_location company_size monthly_expensies \n", + "0 DE L 1382 \n", + "1 JP S 1405 \n", + "2 GB M 1441 \n", + "3 HN S 987 \n", + "4 US L 1182 \n", + ".. ... ... ... \n", + "67 US M 1474 \n", + "68 US S 1301 \n", + "69 AT S 1170 \n", + "70 LU S 1267 \n", + "71 FR S 1127 \n", + "\n", + "[72 rows x 12 columns]" + ] + }, + "execution_count": 1313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Coloque seu código aqui!\n", + "df2020 = df.loc[df.work_year == 2020, :]\n", + "df2020" + ] + }, + { + "cell_type": "code", + "execution_count": 1314, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1.500000e+01\n", + "mean 3.819467e+05\n", + "std 7.527916e+05\n", + "min 2.000000e+04\n", + "25% 4.450000e+04\n", + "50% 9.800000e+04\n", + "75% 3.745000e+05\n", + "max 3.000000e+06\n", + "Name: salary, dtype: float64" + ] + }, + "execution_count": 1314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.loc[df2020.remote_ratio == 0, :].salary.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 1315, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(df2020.loc[df2020.remote_ratio == 0, :].salary)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 1316, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 2.100000e+01\n", + "mean 6.282619e+05\n", + "std 2.378661e+06\n", + "min 8.000000e+03\n", + "25% 4.200000e+04\n", + "50% 7.000000e+04\n", + "75% 1.500000e+05\n", + "max 1.100000e+07\n", + "Name: salary, dtype: float64" + ] + }, + "execution_count": 1316, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.loc[df2020.remote_ratio == 50, :].salary.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 1317, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(df2020.loc[df2020.remote_ratio == 50, :].salary)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 1318, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 3.600000e+01\n", + "mean 2.391419e+05\n", + "std 7.283401e+05\n", + "min 1.000000e+04\n", + "25% 6.000000e+04\n", + "50% 1.015000e+05\n", + "75% 1.357500e+05\n", + "max 4.450000e+06\n", + "Name: salary, dtype: float64" + ] + }, + "execution_count": 1318, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.loc[df2020.remote_ratio == 100, :].salary.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 1319, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(df2020.loc[df2020.remote_ratio == 100, :].salary)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Voltar ao Índice](#indice)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/df_simulado2.xlsx b/df_simulado2.xlsx new file mode 100644 index 0000000..8e87037 Binary files /dev/null and b/df_simulado2.xlsx differ diff --git "a/lista1/An\303\241lise Explorat\303\263ria GABARITO.html" "b/lista1/An\303\241lise Explorat\303\263ria GABARITO.html" new file mode 100644 index 0000000..bad8120 --- /dev/null +++ "b/lista1/An\303\241lise Explorat\303\263ria GABARITO.html" @@ -0,0 +1,16375 @@ + + + + +Análise Exploratória GABARITO + + + + + + + + + + + + + + + + + + + + + + +
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Análise Exporatória

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Índice

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In [2]:
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%matplotlib inline
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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
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Questão 1

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Fonte: https://commons.wikimedia.org/wiki/File:Spotify_Premium_stored-value_cards,_ALDI_Winschoten_(2019).jpg

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Um competidor do Kaggle, George McIntire, que se diz ser apaixonado por músicas, ficou curioso para saber porquê ele gosta das músicas que ele gosta. +Com isso, ele construiu um conjunto de dados de músicas de 2017 obtido via API do Spotify e rotulou cada música como "Like", o que significa que ele gosta e "Dont Like" para músicas que não gosta.

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Fonte: Textos extraídos e/ou adaptados de https://www.kaggle.com/geomack/spotifyclassification.

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Consulte o dicionario de dados no link Dicionário de dados antes de resolver os itens a seguir.

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dados_1 = pd.read_excel("arquivos/Q1_Spotify_CD.xlsx")
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In [4]:
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dados_1.head(3)
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music_idacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencesong_titleartistliking
000.01020.8332046000.4340.0219000.165-8.7950.4310150.0620.286Mask OffFutureLike
110.19900.7433269330.3590.0061100.137-10.4010.0794160.0830.588RedboneChildish GambinoDont Like
220.03440.8381857070.4120.0002340.159-7.1480.289075.0440.173Xanny FamilyFutureLike
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dados_1.liking = dados_1.liking.astype('category')
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dados_1.columns
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Out[6]:
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Index(['music_id', 'acousticness', 'danceability', 'duration_ms', 'energy',
+       'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo',
+       'valence', 'song_title', 'artist', 'liking'],
+      dtype='object')
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A seção a seguir contém uma análise das variáveis energy e liking, por favor faça:

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a) Construa um histograma de densidade de frequências da variável energy separadamente para cada categoria de liking.

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Resposta a partir daqui

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i) Gráficos:

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dados_1.energy.describe()
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Out[7]:
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count    2017.000000
+mean        0.681577
+std         0.210273
+min         0.014800
+25%         0.563000
+50%         0.715000
+75%         0.846000
+max         0.998000
+Name: energy, dtype: float64
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faixa=np.arange(0,1,0.04)
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#Uma maneira:
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+fig = plt.figure(figsize=(15, 4))
+plt.subplot(121)
+dados_1.energy[dados_1.liking=='Dont Like'].plot.hist(bins=faixa, color=('blue'), density=1, title="Dont Like", alpha=0.6)
+plt.ylim(0,2.8)
+plt.ylabel('Densidade')
+plt.xlabel('energy')
+
+plt.subplot(122)
+dados_1.energy[dados_1.liking=='Like'].plot.hist(bins=faixa, color=('red'), density=1, title="Like", alpha=0.6)
+plt.ylim(0,2.8)
+plt.ylabel('Densidade')
+plt.xlabel('energy')
+
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

b) Interprete os histogramas do item anterior em termos da preferência de George. Existe alguma tendência ou orientação em suas preferências?

+ +
+
+
+
+
+
+

Resposta a partir daqui

+ +
+
+
+
+
+
+
    +
  • Se uma faixa tiver energy abaixo de $0.3$ dificilmente Gerge não irá gostar.
    • +
  • Se uma faixa tiver energy acima de $0.8$ possivelmente Gerge irá gostar.
    • +
+ +
+
+
+
+
+
+
+

c) Observando o formato de cada histograma construído anteriormente e utilizando medidas de posição ou centralidade adequadamente, classifique como sendo simétrico, assimétrico a direita ou assimétrico a esquerda. Argumente claramente o motivo de sua classificação. +Apresente as referidas medidas de posição (tendência central)

+ +
+
+
+
+
+
+

Resposta a partir daqui

+ +
+
+
+
+
+
+

Formato do gráfico com uso de medidas de posição!

+ +
+
+
+
+
+
In [10]:
+
+
+
dados_1.energy[dados_1.liking=="Dont Like"].describe()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[10]:
+ + + + +
+
count    381.000000
+mean       0.439961
+std        0.244511
+min        0.014800
+25%        0.247000
+50%        0.445000
+75%        0.629000
+max        0.965000
+Name: energy, dtype: float64
+
+ +
+ +
+
+ +
+
+
+
In [11]:
+
+
+
dados_1.energy[dados_1.liking=="Like"].describe()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[11]:
+ + + + +
+
count    1636.000000
+mean        0.737846
+std         0.154438
+min         0.256000
+25%         0.630000
+50%         0.756500
+75%         0.862000
+max         0.998000
+Name: energy, dtype: float64
+
+ +
+ +
+
+ +
+
+
+
+

Análise das variáveis energy e loudness

+

Neste item, considere as variáveis energy e loudness (utilize todas as faixas sem separar por liking).

+

A partir dos dados, faça:

+ +
+
+
+
+
+
+
+

d) Apresente um gráfico adequado que permita visualizar a associação entre as variáveis energy e loudness. Discuta se há uma associação aparente a partir do gráfico

+ +
+
+
+
+
+
+

Resposta a partir daqui

+ +
+
+
+
+
+
In [12]:
+
+
+
fig = plt.figure(figsize=(15, 4))
+plt.subplot(121)
+plt.scatter(dados_1.energy, dados_1.loudness)
+plt.xlabel('energy = atividade/barulhenta')
+plt.ylabel('loudness = qualidade em dB')
+
+plt.subplot(122)
+plt.scatter(dados_1.loudness, dados_1.energy)
+plt.ylabel('energy = atividade/barulhenta')
+plt.xlabel('loudness = qualidade em dB');
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Nota-se que quanto mais barulhenta (alta energia) for uma faixa maior, tende a ser sua qualidade (dB tendendo a zero). +Uma faixa com qualidade abaixo de -20, tende a ter energia mais próxima de zero.

+ +
+
+
+
+
+
+
+

e) Utilizando uma medida-resumo adequada, discuta o grau de associação dessas duas variáveis: energy e loudness.

+ +
+
+
+
+
+
+

Resposta a partir daqui

+ +
+
+
+
+
+
In [13]:
+
+
+
dados_1.energy.corr(dados_1.loudness)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[13]:
+ + + + +
+
0.7621769711494923
+
+ +
+ +
+
+ +
+
+
+
+

Como já notado graficamente, observa-se que há uma forte associação positiva entre essas duas variáveis (0,76), ainda que não seja necessariamente apenas linear.

+ +
+
+
+
+
+
+
+

Questão 2

Dados da PNAD 2015

+
+
+
+
+
+
+

Mulher Brasileira Chefe de Família. Que Mulher é essa?

+ +
+
+
+
+
+
+

Assuma que haja o interesse em descrever o perfil das mulheres casadas e chefes de família1 no ano de 2015 contrapondo àquelas que também são casadas e trabalham, mas não assumem esse papel na família.

+

A base de dados que terá acesso traz informações exclusivamente de uma amostra restrita de mulheres casadas que possuem as seguintes características:

+

✔ Todas possuem pelo menos um trabalho principal no dia da pesquisa;

+

✔ São todas casadas e têm idade entre 20 a 59 anos;

+

✔ Todas possuem rendimento do trabalho principal entre R\$ 200,00 e R\\$ 5.000,00;

+

✔ Cada mulher tem classificada sua condição na família bem definida na data da pesquisa: Chefe ou Não chefe.

+

1Indivíduo que se declara como “pessoa de referência” na família.

+ +
+
+
+
+
+
+

O arquivo pnadMulher.xlsx traz informações das seguintes variáveis:

+ +
+
+
+
+
+
+ + +
+ +- **REGIAO – indica a região de residência da mulher** + * categorias: ('Norte', 'Nordeste', 'Sul', 'Sudeste', 'Centro-Oeste') + +- **AREA – indica a situação censitária da residência da mulher** + * categorias: ('Rural', 'Urbana') + +- **CONDFAM – corresponde a condição da mulher na famíli** + * categorias: ('Chefe','Nao chefe') + +- **IDADE – idade da mulher**, em anos + +- **CORRACA – cor ou raça da mulher** + * categorias: ('Indigena', 'Branca', 'Preta', 'Amarela', 'Parda') + +- **NATUNIAO – natureza da união** + * categorias: ('Casamento civil e religioso', 'So casamento civil', 'So casamento religioso','Uniao consensual') + +- **NIVELINST – nível de instrução mais elevado alcançado pela mulher** + * categorias: ('Sem instrucao', 'Fundamental incompleto', 'Fundamental completo', + 'Medio incompleto', 'Medio completo', 'Superior incompleto', 'Superior completo', + 'Nao determinado') + +- **OCUPACAO – ocupação da mulher no trabalho principal** + * categorias: ('Empregado', 'Trabalhador domestico', 'Conta propria', 'Empregador') + +- **ANOSTRAB – número de anos no trabalho principal**, em anos + +- **HORASTRABSEMANA – horas habitualmente trabalhadas por semana em todos os trabalhos** + * categorias: ('Ate 14 horas', '15 a 39 horas', '40 a 44 horas', '45 a 48 horas', '49 horas ou mais') + +- **RENDTRABPRINC – rendimento mensal do trabalho principal da mulher**, em reais + +- **RENDTRABTODOS – rendimento mensal de todos os trabalhos da mulher**, em reais + +- **RENDFAMPERCAPITA – rendimento mensal familiar per capita – soma todas as rendas da família**, em reais + +
+
+
+
+
+
+
In [3]:
+
+
+
dados = pd.read_excel("arquivos/pnadMulher.xlsx")
+
+ +
+
+
+ +
+
+
+
In [4]:
+
+
+
dados.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[4]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
REGIAOAREACONDFAMIDADECORRACANATUNIAONIVELINSTOCUPACAOANOSTRABHORASTRABSEMANARENDTRABPRINCRENDTRABTODOSRENDFAMPERCAPITA
0NorteUrbanaChefe35PardaSo casamento civilSuperior incompletoEmpregado040 a 44 horas880880793
1NorteUrbanaChefe34PardaUniao consensualMedio completoEmpregado040 a 44 horas790790818
2NorteUrbanaNao chefe51PardaSo casamento civilMedio completoEmpregador1140 a 44 horas200020001716
3NorteUrbanaNao chefe20PardaUniao consensualMedio incompletoEmpregado040 a 44 horas850850666
4NorteUrbanaChefe49PardaUniao consensualFundamental incompletoConta propria945 a 48 horas250025001662
+
+
+ +
+ +
+
+ +
+
+
+
In [4]:
+
+
+
dados.dtypes
+
+ +
+
+
+ +
+
+ + +
+ +
Out[4]:
+ + + + +
+
REGIAO              object
+AREA                object
+CONDFAM             object
+IDADE                int64
+CORRACA             object
+NATUNIAO            object
+NIVELINST           object
+OCUPACAO            object
+ANOSTRAB             int64
+HORASTRABSEMANA     object
+RENDTRABPRINC        int64
+RENDTRABTODOS        int64
+RENDFAMPERCAPITA     int64
+dtype: object
+
+ +
+ +
+
+ +
+
+
+
+
+

Assuma que o objetivo para trabalhar com a base de dados aqui descrita seja:

“Analisar se as mulheres com condição de chefe e de não chefe na família possuem o mesmo perfil com relação à idade”

a) Utilizando alguma ferramenta estatística que seja adequada, construa um gráfico que permita analisar como a idade pode ser semelhante ou não entre as mulheres com condição de chefe e de não chefe na família. +Ainda, interprete o gráfico em termos do problema (de forma clara e sucinta).

+ +
+
+
+
+
+
+

Resposta:

+ +
+
+
+
+
+
+

A seguir, há algumas opções gráficas corretas que podem ser consideradas para responder tal objetivo.

+ +
+
+
+
+
+
In [5]:
+
+
+
faixa=range(20,61,2)
+
+ +
+
+
+ +
+
+
+
In [6]:
+
+
+
#Uma maneira:
+
+fig = plt.figure(figsize=(15, 5))
+plt.subplot(121)
+dados.IDADE[dados.CONDFAM=='Chefe'].plot.hist(bins=faixa, 
+                                                      color=('blue'), density=1, 
+                                                      title="MULHER CHEFE", alpha=0.6)
+plt.ylabel('Densidade')
+plt.xlabel('IDADE (em anos)')
+
+plt.subplot(122)
+dados.IDADE[dados.CONDFAM=='Nao chefe'].plot.hist(bins=faixa, 
+                                                          color=('blue'), density=1, 
+                                                          title="MULHER NAO CHEFE", alpha=0.6)
+plt.ylabel('Densidade')
+plt.xlabel('IDADE (em anos)')
+
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [7]:
+
+
+
fig = plt.figure(figsize=(15, 7))
+plt.subplot(111)
+dados.IDADE[dados.CONDFAM=='Chefe'].plot.hist(bins=faixa, 
+                                                      color=('blue'), density=1, alpha=0.6)
+dados.IDADE[dados.CONDFAM=='Nao chefe'].plot.hist(bins=faixa, 
+                                                          color=('red'), density=1, alpha=0.6)
+                                                          
+plt.ylabel('Densidade')
+plt.xlabel('IDADE (em anos)')
+plt.legend(('MULHER CHEFE', 'MULHER NÃO CHEFE'))
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Interpretação:

+

Descrever de forma clara e sucinta como IDADE tem suas diferenças ANALISANDO gráfico.

+

Um exemplo: Histograma construído com bins adequados mostrará que mulheres chefes são mais concentradas entre 34 a 40 anos e mulheres não chefes, entre 32 a 38 anos. Ainda, que entre 24 a 36 anos, mulheres não chefes são mais frequentes dos que mulheres chefes. Já entre 54 a 59 anos, mulheres chefes são mais frequentes do que as não chefes. De maneira geral, há uma maior concentração de mulheres chefes em idades mais altas do que as não chefes.

+ +
+
+
+
+
+
+
+

Assuma que o objetivo para trabalhar com a base de dados aqui descrita seja:

Prever a renda mensal familiar per capita em função da renda mensal do trabalho principal da mulher com condição de chefe ne família.”.

b) Faça um gráfico adequado que permita visualizar como é a associação entre as variáveis renda mensal familiar per capita e renda mensal do trabalho principal, considerando apenas as mulheres com condição de chefe na família. Ainda, interprete o gráfico em termos do problema (de forma clara e sucinta).

+ +
+
+
+
+
+
In [5]:
+
+
+
fig = plt.figure(figsize=(9, 5))
+
+plt.scatter(dados.RENDTRABPRINC[dados.CONDFAM=="Chefe"], dados.RENDFAMPERCAPITA[dados.CONDFAM=="Chefe"], c='cyan', alpha=0.8)
+plt.title('RENDFAMPERCAPITA explicada por RENDTRABPRINC')
+plt.xlabel('em reais')
+plt.ylabel('em reais')
+
+plt.tight_layout()
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

c) Calcule uma medida adequada que permita interpretar o grau de associação entre as variáveis renda mensal familiar per capita e renda mensal do trabalho principal, considerando apenas as mulheres com condição de chefe na família.

+ +
+
+
+
+
+
In [6]:
+
+
+
print(dados.RENDFAMPERCAPITA[dados.CONDFAM=="Chefe"].corr(dados.RENDTRABPRINC[dados.CONDFAM=="Chefe"]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
0.6233913998885166
+
+
+
+ +
+
+ +
+
+
+
+

Interpretação: PARA PROVA, APENAS MULHERES CHEFES

+

i) Descrever associação positiva visualizada em gráfico de dispersão, considerando obrigatoriamente variável RENDTRABPRINC em eixo x e RENDFAMPERCAPITA em eixo y.

+

ii) Correlação entre RENDTRABPRINC e RENDFAMPERCAPITA.

+ +
+
+
+
+
+
+
+

QUESTÃO 3

Análise do ENEM

+
+
+
+
+
+
+
+ +
+
+
+
+
+
+

Esta parte da avaliação se baseia em uma amostra de dados do ENEM 2017 adaptados para esta prova. O dicionario de dados encontra-se no link a seguir:

+

Dicionário de dados

+ +
+
+
+
+
+
In [7]:
+
+
+
filename = "arquivos/enem2017_30K.csv"
+dados = pd.read_csv(filename, index_col=0)
+dados.head(5)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[7]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NU_INSCRICAOSG_UF_RESIDENCIANU_IDADETP_SEXOTP_ESTADO_CIVILTP_ST_CONCLUSAOIN_TREINEIRONU_NOTA_CNNU_NOTA_CHNU_NOTA_LC...CARROCELULARFIXOCOMPUTADORINTERNETQ026TIPO_EMRENDANOTA_TESTESNOTA_ENEM
171957170003469518MT26.0F0.010605.4657.4542.4...AEABAAC3020.0915752430.92990.9
127898170005286919CE17.0M0.020698.5649.0630.6...CBABBBD3611.5897802817.13677.1
132925170005113172MA21.0M0.010591.0643.8597.9...ADABBAA1375.4541822385.83325.8
119870170005346442PR17.0F0.020525.6441.2509.4...BCABBBA2079.5164691930.02450.0
122904170004476042DF26.0F0.010538.0408.8523.9...BCAAAAA1364.0951191921.82381.8
+

5 rows × 28 columns

+
+
+ +
+ +
+
+ +
+
+
+
+

a) Apresente uma tabela cruzada que mostre a escolaridade do pai cruzada com o tipo do ensino médio. Aponte a combinação de categorias mais frequente e, baseado no dicionário de dados, explique o que são.

+ +
+
+
+
+
+
In [13]:
+
+
+
ct = pd.crosstab(dados.ESC_PAI, dados.TIPO_EM, normalize=True)
+
+ +
+
+
+ +
+
+
+
In [14]:
+
+
+
ct*100
+
+ +
+
+
+ +
+
+ + +
+ +
Out[14]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TIPO_EMABCDE
ESC_PAI
A5.0333330.0633330.0566670.0633330.046667
B20.5233330.3933330.2600000.4366670.253333
C13.2233330.3566670.2066670.6300000.273333
D10.4500000.4233330.1700000.9966670.320000
E18.2200001.2200000.4833334.4666671.070000
F3.0333330.4200000.1133333.3333330.460000
G1.1933330.2533330.0566672.6533330.256667
H7.6433330.2500000.0900000.4433330.160000
+
+
+ +
+ +
+
+ +
+
+
+
+

O crosstab mostra que $20.53\%$ das pessoas têm escolaridade do pai B, ou seja, não completou o 5.o ano do fundamental ao mesmo tempo que tipo do ensino médio A, ou seja, estuda em escola pública.

+ +
+
+
+
+
+
+

b) Baseado nos dados, encontre a idade da mulher mais idosa que prestou a prova

+ +
+
+
+
+
+
In [15]:
+
+
+
max(dados[dados.TP_SEXO=="F"].NU_IDADE)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[15]:
+ + + + +
+
74.0
+
+ +
+ +
+
+ +
+
+
+
+

c) Avalie a afirmação: pessoas com mais renda tendem a ir melhor no ENEM

+

É necessário:

+
    +
  • realizar o plot de dispersão renda versus pontuação
    • +
+ +
+
+
+
+
+
In [8]:
+
+
+
dados.plot.scatter(x="RENDA", y="NOTA_ENEM")
+
+ +
+
+
+ +
+
+ + +
+ +
Out[8]:
+ + + + +
+
<matplotlib.axes._subplots.AxesSubplot at 0x7f93f2e909d0>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [17]:
+
+
+
dados.RENDA.corr(dados.NOTA_ENEM)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[17]:
+ + + + +
+
0.4081900792396614
+
+ +
+ +
+
+ +
+
+
+
+

Existe uma correlação de intensidade média entre RENDA e NOTA_ENEM. O valor do coeficiente de correlação é 0.4

+

Nota-se no gráfico que as notas do ENEM variam bastante em todos os níveis de renda

+ +
+
+
+
+
+
+

d) Compare as várias categorias do ensino médio. Use um recurso gráfico que permita visualizar a distribuição das notas do ENEM comparando as categorias de ensino médio.

+ +
+
+
+
+
+
+
+
    +

  • A Somente em escola pública.

    +
    • +

  • B Parte em escola pública e parte em escola privada SEM bolsa de estudo integral.

    +
    • +

  • C Parte em escola pública e parte em escola privada COM bolsa de estudo integral.

    +
    • +

  • D Somente em escola privada SEM bolsa de estudo integral.

    +
    • +

  • E Somente em escola privada COM bolsa de estudo integral.

    +
    • +
+
+ +
+
+
+
+
+
In [29]:
+
+
+
# PROF
+from numpy import arange
+minimo = dados.NOTA_ENEM.min() - 10
+maximo = dados.NOTA_ENEM.max() + 10
+faixa = arange(minimo,maximo,100)
+plt.figure(figsize=(15, 10))
+
+############################################################
+plt.subplot(231)
+plt.hist(dados.loc[dados.TIPO_EM == "A","NOTA_ENEM"], 
+         edgecolor='white',density=True, color='#EC4889', 
+         linewidth=1, alpha=0.8, bins=faixa)
+plt.ylabel('densidade')
+plt.xlabel('notas')
+plt.title("Tipo A")
+plt.xlim(minimo,maximo)
+
+############################################################
+plt.subplot(232)
+plt.hist(dados.loc[dados.TIPO_EM == "B","NOTA_ENEM"], 
+         edgecolor='white', density=True, color='#7895E9', 
+         linewidth=1, alpha=0.8, bins=faixa)
+plt.ylabel('densidade')
+plt.xlabel('notas')
+plt.title("Tipo B")
+plt.xlim(minimo,maximo)
+
+############################################################
+plt.subplot(233)
+plt.hist(dados.loc[dados.TIPO_EM == "C","NOTA_ENEM"], 
+        edgecolor='white', density=True, color='#C361EB', 
+        linewidth=1, alpha=0.8, bins=faixa)
+plt.ylabel('densidade')
+plt.xlabel('notas')
+plt.title("Tipo C")
+plt.xlim(minimo,maximo)
+
+############################################################
+plt.subplot(234)
+plt.hist(dados.loc[dados.TIPO_EM == "D","NOTA_ENEM"], 
+         edgecolor='white', density=True, color='#ECC948', 
+        linewidth=1, alpha=0.8, bins=faixa)
+plt.ylabel('densidade')
+plt.xlabel('notas')
+plt.title("Tipo D")
+plt.xlim(minimo,maximo)
+
+############################################################
+plt.subplot(235)
+plt.hist(dados.loc[dados.TIPO_EM == "E","NOTA_ENEM"], 
+        edgecolor='white', density=True, color='#48ECCC', 
+        linewidth=1, alpha=0.8, bins=faixa)
+plt.ylabel('densidade')
+plt.xlabel('notas')
+plt.title("Tipo E")
+plt.xlim(minimo,maximo)
+
+plt.tight_layout()
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
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+ +
+
+
+
In [30]:
+
+
+
#Outra maneira:
+plt.figure(figsize=(25,25))
+dados.boxplot(column="NOTA_ENEM", by="TIPO_EM")
+plt.tight_layout()
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+
<Figure size 1800x1800 with 0 Axes>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Resposta: Existe diferença entre os tipos de ensino médio de acordo com a nota no ENEM. Em particular, podemos ressaltar que:

+
    +

  • Alunos de escolas privadas tendem a ter maiores notas do que alunos da rede publica de ensino, ou aqueles que cursaram parte de seu estudo na rede publica.

    +
    • +

  • Alunos bolsistas da rede privada tendem a ter notas menores no ENEM que alunos sem bolsa, mas essa diferença não é muito grande.

    +
    • +
+ +
+
+
+
+
+
+
+

KAGGLE:

+
+
+
+
+
+
+

+ +
+
+
+
+
+
+

O Kaggle.com é um site de competições internacionais de Data Science no qual empresas oferecem prêmios para os analistas que fizerem a melhor modelagem preditiva de diversos problemas.

+

Em uma das competições recente, o desafio era:

+

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

+

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

+
+

A seguir, vocês receberão um conjunto de dados bem menor em termos de número de variáveis disponível no Kaggle.

+ +
+
+
+
+
+
+

SIGNIFICADO DAS VARIÁVEIS

A base de dados que terão acesso traz informações de 1460 imóveis cujas variáveis selecionadas possuem as seguintes descrições traduzidas (mantendo os nomes das variáveis e das categorias do problema original - em inglês):

+ +
+
+
+
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+
+

✔ Id: número que identifica o imóvel na base de dados.

+

✔ SalePrice: preço de venda da propriedade (em dólares). Ess +a é a variável alvo que você irá tentar predizer.

+

✔ Foundation: tipo de fundação, com as seguintes categorias:

+
    +
  • BrkTil - Tijolo e telha
    • +
  • CBlock - Bloco de concreto
    • +
  • PConc - Concreto usinado
    • +
  • Slab - Laje
    • +
  • Stone - Pedra
    • +
  • Wood - Madeira
    • +
+

✔ OverallQual: avalia de maneira geral o material e o acabamento da casa, com as seguintes categorias:

+
    +
  • Very Poor - Péssimo
    • +
  • Poor - Ruim
    • +
  • Fair - Justo
    • +
  • Below Average - Abaixo da média
    • +
  • Average - Médio
    • +
  • Above Average - Acima da média
    • +
  • Good - Bom
    • +
  • Very Good - Muito bom
    • +
  • Excellent - Excelente
    • +
  • Very Excellent - Muito excelente
    • +
+

✔ LotArea: tamanho do lote (em pés quadrados).

+

✔ X1stFlrSF: tamanho do primeiro andar (em pés quadrados).

+

✔ X2ndFlrSF: tamanho do segundo andar (em pés quadrados).

+

✔ GarageCars: tamanho da garagem em capacidade de carros (número de carros).

+

✔ Fireplaces: número de lareiras.

+

✔ Street: tipo de acesso rodoviário à propriedade, com as seguintes categorias:

+
    +
  • Grvl - Cascalho
    • +
  • Pave - Pavimentado
    • +
+

✔ LandSlope: inclinação da propriedade, com as seguintes categorias:

+
    +
  • Gtl - inclinação suave
    • +
  • Mod - inclinação moderada
    • +
  • Sev - severo decline
    • +
+

✔ RoofStyle: tipo do telhado, com as seguintes categorias:

+
    +
  • Flat - Telhado plano
    • +
  • Gable - Telhado de duas águas
    • +
  • Gambrel - Telhado estilo celeiro
    • +
  • Hip - Telhado de ripa
    • +
  • Mansard - Mansarda
    • +
  • Shed - Galpão
    • +
+

✔ CentralAir: ar condicionado central, com as seguintes categorias:

+
    +
  • N - Não
    • +
  • Y - Sim
    • +
+

✔ YearBuilt: ano da construção do imóvel.

+

✔ YrSold: ano de venda do imóvel.

+

O arquivo <house.xlsx > traz todas essas 15 variáveis.

+ +
+
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+
+
+
In [9]:
+
+
+
dados = pd.read_excel("arquivos/house.xlsx")
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+ +
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+ +
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+
In [3]:
+
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dados.head()
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+ +
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+ +
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+ + +
+ +
Out[3]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
IdSalePriceFoundationOverallQualLotAreaX1stFlrSFX2ndFlrSFGarageCarsFireplacesStreetLandSlopeRoofStyleCentralAirYearBuiltYrSold
01208500PConcGood845085685420PaveGtlGableY20032008
12181500CBlockAbove Average96001262021PaveGtlGableY19762007
23223500PConcGood1125092086621PaveGtlGableY20012008
34140000BrkTilGood955096175631PaveGtlGableY19152006
45250000PConcVery Good142601145105331PaveGtlGableY20002008
+
+
+ +
+ +
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+ +
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+
In [4]:
+
+
+
dados.dtypes
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+ +
+
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+ +
+
+ + +
+ +
Out[4]:
+ + + + +
+
Id              int64
+SalePrice       int64
+Foundation     object
+OverallQual    object
+LotArea         int64
+X1stFlrSF       int64
+X2ndFlrSF       int64
+GarageCars      int64
+Fireplaces      int64
+Street         object
+LandSlope      object
+RoofStyle      object
+CentralAir     object
+YearBuilt       int64
+YrSold          int64
+dtype: object
+
+ +
+ +
+
+ +
+
+
+
In [5]:
+
+
+
dados.Foundation = dados.Foundation.astype('category')
+dados.OverallQual = dados.OverallQual.astype('category')
+dados.Street = dados.Street.astype('category')
+dados.LandSlope = dados.LandSlope.astype('category')
+dados.RoofStyle = dados.RoofStyle.astype('category')
+dados.CentralAir = dados.CentralAir.astype('category')
+
+dados.Foundation.cat.categories = (['BrkTil','CBlock','PConc','Slab','Stone','Wood'])
+dados.OverallQual.cat.categories = (['Very Poor','Poor','Fair','Below Average','Average',
+                                     'Above Average','Good','Very Good','Excellent','Very Excellent'])
+
+dados.Street.cat.categories = (['Grvl','Pave'])
+dados.LandSlope.cat.categories = (['Gtl','Mod','Sev'])
+dados.RoofStyle.cat.categories = (['Flat','Gable','Gambrel','Hip','Mansard','Shed'])
+dados.CentralAir.cat.categories = (['N','Y'])
+
+ +
+
+
+ +
+
+
+
+
+

Questão 4

+
+
+
+
+
+
+
+

Assuma que o objetivo para trabalhar com a base de dados acima seja: “Analisar como uma determinada variável pode impactar sobre o preço de vendas de um imóvel”.

a) Utilizando alguma ferramenta estatística (gráfica) que seja adequada, analise como a presença ou não de ar central no imóvel (variável CentralAir) pode impactar sobre preço de vendas de um imóvel (variável SalePrice). A interpretação precisa ser completa PORÉM sucinta.

+ +
+
+
+
+
+
In [6]:
+
+
+
dados.SalePrice.describe()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[6]:
+ + + + +
+
count      1460.000000
+mean     180921.195890
+std       79442.502883
+min       34900.000000
+25%      129975.000000
+50%      163000.000000
+75%      214000.000000
+max      755000.000000
+Name: SalePrice, dtype: float64
+
+ +
+ +
+
+ +
+
+
+
In [7]:
+
+
+
faixa=range(34000,800000,50000)
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
#Uma maneira:
+
+fig = plt.figure(figsize=(15, 4))
+plt.subplot(121)
+dados.SalePrice[dados.CentralAir=='N'].plot.hist(bins=faixa, color=('blue'), density=True, title="IMOVEL SEM AR", alpha=0.6)
+plt.ylabel('Densidade')
+plt.xlabel('Preço de venda do imóvel (em dolares)')
+
+plt.subplot(122)
+dados.SalePrice[dados.CentralAir=='Y'].plot.hist(bins=faixa, color=('blue'), density=True, title="IMOVEL COM AR", alpha=0.6)
+plt.ylabel('Densidade')
+plt.xlabel('Preço de venda do imóvel (em dolares)')
+
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [45]:
+
+
+
#Outra maneira:
+dados.boxplot(column="SalePrice",by="CentralAir")
+plt.tight_layout()
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [40]:
+
+
+
#Descrever da forma mais completa como presença de Air Central pode predizer preços de imoveis maiores do que sem Ar Central.
+#Deixar claro que é maior em média, ou em a partir da haste superior do SEM AR encontra-se possiveis aberrantes enquanto que nos imoveis 
+#COM AR encontra-se 50% dos imoveis mais caros.
+
+ +
+
+
+ +
+
+
+
+
+

Assuma que o objetivo para trabalhar com a base de dados acima seja: “Analisar como uma determinada variável pode impactar sobre o preço de vendas de um imóvel”.

b) Utilizando alguma ferramentas estatísticas (numérica E gráfica) que sejam adequadas, analise como o tamanho de área construída do primeiro andar do imóvel (variável X1stFlrSF) pode impactar sobre preço de vendas do mesmo (variável SalePrice). A interpretação precisa ser completa PORÉM sucinta.

+ +
+
+
+
+
+
In [11]:
+
+
+
dados.SalePrice.corr(dados.X1stFlrSF)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[11]:
+ + + + +
+
0.6058521846919146
+
+ +
+ +
+
+ +
+
+
+
In [10]:
+
+
+
fig = plt.figure(figsize=(9, 5))
+
+plt.scatter(dados.X1stFlrSF, dados.SalePrice, c='cyan', alpha=0.8)
+plt.title('SalePrice explicado por X1st')
+plt.xlabel('em sqft')
+plt.ylabel('em dolares')
+
+plt.tight_layout()
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

c) Como você verificaria a seguinte afirmação: Os imóveis sem ar central possuem a mesma qualidade geral de material e de acabamento quando comparados com imóveis que possuem ar central?

+

Obs: Considere as variáveis CentralAir e OverallQual para responder a pergunta acima.

+ +
+
+
+
+
+
In [13]:
+
+
+
#pode ser apenas tabela
+ct2=(pd.crosstab(dados.CentralAir, dados.OverallQual, margins=False, 
+                 rownames=['Ar Central'], colnames=['Qualidade'], normalize='index')*100).round(decimals=2)
+ct2
+
+ +
+
+
+ +
+
+ + +
+ +
Out[13]:
+ + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
QualidadeVery PoorPoorFairBelow AverageAverageAbove AverageGoodVery GoodExcellentVery Excellent
Ar Central
N18.9533.6824.210.0011.585.263.160.001.052.11
Y26.0826.746.813.150.6623.000.001.3212.230.00
+
+
+ +
+ +
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+ +
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+
+
In [14]:
+
+
+
#pode ter grafico tambem mas não é obrigatório se a tabela for bem interpretada
+
+plot = ct2.plot(kind='bar', stacked=True,title='Qualidade por Ar Central',figsize=(8, 6))
+plt.ylabel('Freq.Relativa (em %)')
+
+#Para legenda ficar fora do gráfico
+ax = plt.subplot(111)
+box = ax.get_position()
+ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
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+ +
+
+
+ + + + + + diff --git "a/lista1/An\303\241lise Explorat\303\263ria.ipynb" "b/lista1/An\303\241lise Explorat\303\263ria.ipynb" new file mode 100644 index 0000000..555eb0a --- /dev/null +++ "b/lista1/An\303\241lise Explorat\303\263ria.ipynb" @@ -0,0 +1,1991 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Análise Exporatória" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "## Índice\n", + "\n", + "- [Questão 1](#q1)\n", + " - [Item a](#q1_a)\n", + " - [Item b](#q1_b)\n", + " - [Item c](#q1_c)\n", + " - [Item d](#q1_d)\n", + " - [Item e](#q1_e)\n", + "- [Questão 2](#q2)\n", + " - [Item a](#q2_a)\n", + " - [Item b](#q2_b)\n", + "- [Questão 3](#q3)\n", + " - [Item a](#q3_a)\n", + " - [Item b](#q3_b)\n", + " - [Item c](#q3_c)\n", + " - [Item d](#q3_d)\n", + "- [Questão 4](#q4)\n", + " - [Item a](#q4_a)\n", + " - [Item b](#q4_b)\n", + " - [Item c](#q4_c)\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": 822, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + "# Questão 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\"Fonte: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Um competidor do Kaggle, George McIntire, que se diz ser apaixonado por músicas, ficou curioso para saber porquê ele gosta das músicas que ele gosta. \n", + "Com isso, ele construiu um conjunto de dados de músicas de 2017 obtido via API do Spotify e rotulou cada música como *\"Like\"*, o que significa que ele gosta e *\"Dont Like\"* para músicas que não gosta. \n", + "\n", + "*Fonte: Textos extraídos e/ou adaptados de [https://www.kaggle.com/geomack/spotifyclassification]([https://www.kaggle.com/geomack/spotifyclassification]).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consulte o dicionario de dados no link [Dicionário de dados](./arquivos/Q1_DicioSpotify.pdf) antes de resolver os itens a seguir.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 823, + "metadata": {}, + "outputs": [], + "source": [ + "dados_1 = pd.read_excel(\"arquivos/Q1_Spotify_CD.xlsx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 824, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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music_idacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencesong_titleartistliking
000.01020.8332046000.4340.0219000.165-8.7950.4310150.0620.286Mask OffFutureLike
110.19900.7433269330.3590.0061100.137-10.4010.0794160.0830.588RedboneChildish GambinoDont Like
220.03440.8381857070.4120.0002340.159-7.1480.289075.0440.173Xanny FamilyFutureLike
\n", + "
" + ], + "text/plain": [ + " music_id acousticness danceability duration_ms energy \\\n", + "0 0 0.0102 0.833 204600 0.434 \n", + "1 1 0.1990 0.743 326933 0.359 \n", + "2 2 0.0344 0.838 185707 0.412 \n", + "\n", + " instrumentalness liveness loudness speechiness tempo valence \\\n", + "0 0.021900 0.165 -8.795 0.4310 150.062 0.286 \n", + "1 0.006110 0.137 -10.401 0.0794 160.083 0.588 \n", + "2 0.000234 0.159 -7.148 0.2890 75.044 0.173 \n", + "\n", + " song_title artist liking \n", + "0 Mask Off Future Like \n", + "1 Redbone Childish Gambino Dont Like \n", + "2 Xanny Family Future Like " + ] + }, + "execution_count": 824, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados_1.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 825, + "metadata": {}, + "outputs": [], + "source": [ + "dados_1.liking = dados_1.liking.astype('category')" + ] + }, + { + "cell_type": "code", + "execution_count": 826, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['music_id', 'acousticness', 'danceability', 'duration_ms', 'energy',\n", + " 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo',\n", + " 'valence', 'song_title', 'artist', 'liking'],\n", + " dtype='object')" + ] + }, + "execution_count": 826, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados_1.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**A seção a seguir contém uma análise das variáveis `energy` e `liking`, por favor faça:**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "**a)** Construa um histograma de densidade de frequências da variável `energy` separadamente para cada categoria de `liking`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta a partir daqui**" + ] + }, + { + "cell_type": "code", + "execution_count": 827, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15, 4))\n", + "plt.subplot(121)\n", + "plt.hist(dados_1.loc[dados_1.liking == \"Like\", :].energy, density=True, bins=np.arange(0,1,0.04), color='blue', edgecolor='black')\n", + "plt.title(\"Like\")\n", + "plt.ylabel(\"Densidade\")\n", + "plt.xlabel(\"Energia\")\n", + "plt.subplot(122)\n", + "plt.hist(dados_1.loc[dados_1.liking == \"Dont Like\", :].energy, density=True, bins=np.arange(0,1,0.04), color='red', edgecolor='black')\n", + "plt.title(\"Don't Like\")\n", + "plt.ylabel(\"Densidade\")\n", + "plt.xlabel(\"Energia\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**b)** Interprete os histogramas do item anterior em termos da preferência de George. Existe alguma tendência ou orientação em suas preferências?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta a partir daqui**" + ] + }, + { + "cell_type": "code", + "execution_count": 828, + "metadata": {}, + "outputs": [], + "source": [ + "# Sim, ele tende a gostar mais de músicas que tenham mais energia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**c)** Observando o formato de cada histograma construído anteriormente e utilizando medidas de posição ou centralidade adequadamente, classifique como sendo simétrico, assimétrico a direita ou assimétrico a esquerda. Argumente claramente o motivo de sua classificação. \n", + "Apresente as referidas medidas de posição (tendência central)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta a partir daqui**" + ] + }, + { + "cell_type": "code", + "execution_count": 829, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 1636.000000\n", + "mean 0.737846\n", + "std 0.154438\n", + "min 0.256000\n", + "25% 0.630000\n", + "50% 0.756500\n", + "75% 0.862000\n", + "max 0.998000\n", + "Name: energy, dtype: float64" + ] + }, + "execution_count": 829, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados_1.loc[dados_1.liking == \"Like\", :].energy.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 830, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 381.000000\n", + "mean 0.439961\n", + "std 0.244511\n", + "min 0.014800\n", + "25% 0.247000\n", + "50% 0.445000\n", + "75% 0.629000\n", + "max 0.965000\n", + "Name: energy, dtype: float64" + ] + }, + "execution_count": 830, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados_1.loc[dados_1.liking == \"Dont Like\", :].energy.describe()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Dados do gráfico like possuem desvio padrão menor do que os do gráfico dont like, indicando uma concentração maior de dados.\n", + "Além disso, o primeiro quartil do gráfico like apresenta valores bem superiores aos primeiro e segundo quartis do gráfico dont like,\n", + "indicando uma assimetria à esquerda do gráfico like e uma simetria do gráfico dont like" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Análise das variáveis energy e loudness**\n", + "\n", + "Neste item, considere as variáveis `energy` e `loudness` (utilize todas as faixas sem separar por `liking`).\n", + "\n", + "A partir dos dados, faça:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "**d)** Apresente um **gráfico** adequado que permita visualizar a associação entre as variáveis `energy` e `loudness`. Discuta se há uma associação aparente a partir do gráfico" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta a partir daqui**" + ] + }, + { + "cell_type": "code", + "execution_count": 831, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "plt.scatter(dados_1.loudness, dados_1.energy)\n", + "plt.title(\"Energia x Volume\")\n", + "plt.xlabel(\"Volume\")\n", + "plt.ylabel(\"Energia\")\n", + "plt.show()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Aparenta haver uma associação, quanto maior o volume médio, maior a tendência de a música ter mais energia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**e)** Utilizando uma **medida-resumo** adequada, discuta o grau de associação dessas duas variáveis: `energy` e `loudness`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta a partir daqui**" + ] + }, + { + "cell_type": "code", + "execution_count": 832, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7621769711494921" + ] + }, + "execution_count": 832, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados_1.loudness.corr(dados_1.energy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "# Questão 2\n", + "### Dados da PNAD 2015\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mulher Brasileira Chefe de Família. Que Mulher é essa? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuma que haja o interesse em descrever o perfil das mulheres casadas e chefes de família1 no ano de 2015 contrapondo àquelas que também são casadas e trabalham, mas não assumem esse papel na família.\n", + "\n", + "A base de dados que terá acesso traz informações exclusivamente de uma amostra restrita de mulheres casadas que possuem as seguintes características:\n", + "\n", + "✔ Todas possuem **pelo menos um trabalho principal** no dia da pesquisa;\n", + "\n", + "✔ São **todas casadas** e têm **idade entre 20 a 59 anos**;\n", + "\n", + "✔ Todas possuem **rendimento do trabalho principal entre R\\\\$ 200,00 e R\\\\$ 5.000,00**;\n", + "\n", + "\n", + "✔ Cada mulher tem classificada **sua condição na família** bem definida na data da pesquisa: **Chefe** ou **Não chefe**.\n", + "\n", + "\n", + "1*Indivíduo que se declara como “pessoa de referência” na família.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O arquivo **pnadMulher.xlsx** traz informações das seguintes variáveis: \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + " \n", + "- **REGIAO – indica a região de residência da mulher**\n", + " * categorias: ('Norte', 'Nordeste', 'Sul', 'Sudeste', 'Centro-Oeste') \n", + "\n", + "- **AREA – indica a situação censitária da residência da mulher**\n", + " * categorias: ('Rural', 'Urbana') \n", + "\n", + "- **CONDFAM – corresponde a condição da mulher na famíli**\n", + " * categorias: ('Chefe','Nao chefe') \n", + "\n", + "- **IDADE – idade da mulher**, em anos \n", + "\n", + "- **CORRACA – cor ou raça da mulher**\n", + " * categorias: ('Indigena', 'Branca', 'Preta', 'Amarela', 'Parda') \n", + "\n", + "- **NATUNIAO – natureza da união**\n", + " * categorias: ('Casamento civil e religioso', 'So casamento civil', 'So casamento religioso','Uniao consensual') \n", + "\n", + "- **NIVELINST – nível de instrução mais elevado alcançado pela mulher**\n", + " * categorias: ('Sem instrucao', 'Fundamental incompleto', 'Fundamental completo',\n", + " 'Medio incompleto', 'Medio completo', 'Superior incompleto', 'Superior completo', \n", + " 'Nao determinado') \n", + "\n", + "- **OCUPACAO – ocupação da mulher no trabalho principal**\n", + " * categorias: ('Empregado', 'Trabalhador domestico', 'Conta propria', 'Empregador') \n", + "\n", + "- **ANOSTRAB – número de anos no trabalho principal**, em anos \n", + "\n", + "- **HORASTRABSEMANA – horas habitualmente trabalhadas por semana em todos os trabalhos**\n", + " * categorias: ('Ate 14 horas', '15 a 39 horas', '40 a 44 horas', '45 a 48 horas', '49 horas ou mais') \n", + "\n", + "- **RENDTRABPRINC – rendimento mensal do trabalho principal da mulher**, em reais \n", + "\n", + "- **RENDTRABTODOS – rendimento mensal de todos os trabalhos da mulher**, em reais \n", + "\n", + "- **RENDFAMPERCAPITA – rendimento mensal familiar per capita – soma todas as rendas da família**, em reais\n", + "\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 833, + "metadata": {}, + "outputs": [], + "source": [ + "dados = pd.read_excel(\"arquivos/pnadMulher.xlsx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 834, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1NorteUrbanaChefe34PardaUniao consensualMedio completoEmpregado040 a 44 horas790790818
2NorteUrbanaNao chefe51PardaSo casamento civilMedio completoEmpregador1140 a 44 horas200020001716
3NorteUrbanaNao chefe20PardaUniao consensualMedio incompletoEmpregado040 a 44 horas850850666
4NorteUrbanaChefe49PardaUniao consensualFundamental incompletoConta propria945 a 48 horas250025001662
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" + ], + "text/plain": [ + " REGIAO AREA CONDFAM IDADE CORRACA NATUNIAO \\\n", + "0 Norte Urbana Chefe 35 Parda So casamento civil \n", + "1 Norte Urbana Chefe 34 Parda Uniao consensual \n", + "2 Norte Urbana Nao chefe 51 Parda So casamento civil \n", + "3 Norte Urbana Nao chefe 20 Parda Uniao consensual \n", + "4 Norte Urbana Chefe 49 Parda Uniao consensual \n", + "\n", + " NIVELINST OCUPACAO ANOSTRAB HORASTRABSEMANA \\\n", + "0 Superior incompleto Empregado 0 40 a 44 horas \n", + "1 Medio completo Empregado 0 40 a 44 horas \n", + "2 Medio completo Empregador 11 40 a 44 horas \n", + "3 Medio incompleto Empregado 0 40 a 44 horas \n", + "4 Fundamental incompleto Conta propria 9 45 a 48 horas \n", + "\n", + " RENDTRABPRINC RENDTRABTODOS RENDFAMPERCAPITA \n", + "0 880 880 793 \n", + "1 790 790 818 \n", + "2 2000 2000 1716 \n", + "3 850 850 666 \n", + "4 2500 2500 1662 " + ] + }, + "execution_count": 834, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 835, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "REGIAO object\n", + "AREA object\n", + "CONDFAM object\n", + "IDADE int64\n", + "CORRACA object\n", + "NATUNIAO object\n", + "NIVELINST object\n", + "OCUPACAO object\n", + "ANOSTRAB int64\n", + "HORASTRABSEMANA object\n", + "RENDTRABPRINC int64\n", + "RENDTRABTODOS int64\n", + "RENDFAMPERCAPITA int64\n", + "dtype: object" + ] + }, + "execution_count": 835, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "#### Assuma que o objetivo para trabalhar com a base de dados aqui descrita seja: \n", + "\n", + "#### “Analisar se as mulheres com condição de chefe e de não chefe na família possuem o mesmo perfil com relação à idade”\n", + "\n", + "**a)** Utilizando alguma ferramenta estatística que seja adequada, construa um **gráfico** que permita analisar como a idade pode ser semelhante ou não entre as mulheres com condição de chefe e de não chefe na família. \n", + "Ainda, interprete o gráfico em termos do problema (de forma clara e sucinta).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Resposta:**" + ] + }, + { + "cell_type": "code", + "execution_count": 836, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 7611.000000\n", + "mean 39.335961\n", + "std 9.524820\n", + "min 20.000000\n", + "25% 32.000000\n", + "50% 39.000000\n", + "75% 47.000000\n", + "max 59.000000\n", + "Name: IDADE, dtype: float64" + ] + }, + "execution_count": 836, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.loc[dados.CONDFAM == \"Chefe\", :].IDADE.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 837, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 21169.000000\n", + "mean 38.852426\n", + "std 9.578980\n", + "min 20.000000\n", + "25% 31.000000\n", + "50% 38.000000\n", + "75% 46.000000\n", + "max 59.000000\n", + "Name: IDADE, dtype: float64" + ] + }, + "execution_count": 837, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.loc[dados.CONDFAM == \"Nao chefe\", :].IDADE.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 838, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15, 4))\n", + "plt.subplot(121)\n", + "plt.hist(dados.loc[dados.CONDFAM == \"Chefe\", :].IDADE, density=True, bins=np.arange(19,60,1), color='blue', edgecolor='black')\n", + "plt.title(\"Chefe\")\n", + "plt.ylabel(\"Densidade\")\n", + "plt.xlabel(\"Idade\")\n", + "plt.subplot(122)\n", + "plt.hist(dados.loc[dados.CONDFAM == \"Nao chefe\", :].IDADE, density=True, bins=np.arange(19,60,1), color='red', edgecolor='black')\n", + "plt.title(\"Não Chefe\")\n", + "plt.ylabel(\"Densidade\")\n", + "plt.xlabel(\"Idade\")\n", + "plt.show()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "As distribuições são muito parecidas, levando a crer que mulheres com condição de chefe e de não chefe possuem um mesmo perfil em relação a idade." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "#### Assuma que o objetivo para trabalhar com a base de dados aqui descrita seja: \n", + "\n", + "#### Prever a renda mensal familiar per capita em função da renda mensal do trabalho principal da mulher com condição de chefe ne família.”.\n", + "\n", + "**b)** Faça um gráfico adequado que permita visualizar como é a associação entre as variáveis renda mensal familiar per capita e renda mensal do trabalho principal, considerando apenas as mulheres com condição de chefe na família. Ainda, interprete o gráfico em termos do problema (de forma clara e sucinta)." + ] + }, + { + "cell_type": "code", + "execution_count": 839, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "plt.scatter(dados.loc[dados.CONDFAM == \"Chefe\", :].RENDTRABPRINC, dados.loc[dados.CONDFAM == \"Chefe\", :].RENDFAMPERCAPITA)\n", + "plt.title(\"Renda mensal familiar per capita x Renda mensal do trabalho principal\")\n", + "plt.xlabel(\"Renda mensal do trabalho principal\")\n", + "plt.ylabel(\"Renda mensal familiar per capita\")\n", + "plt.show()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Existem alguns outliers, e dados muito concentrados para rendas de trabalho principal mais baixas e renda mensal familiar per capta mais baixas,\n", + "mas aparenta existir uma relação moderada entre as variáveis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "**c)** Calcule uma medida adequada que permita interpretar o grau de associação entre as variáveis renda mensal familiar per capita e renda mensal do trabalho principal, considerando apenas as mulheres com condição de chefe na família. \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 840, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6233913998885169" + ] + }, + "execution_count": 840, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.loc[dados.CONDFAM == \"Chefe\", :].RENDTRABPRINC.corr(dados.loc[dados.CONDFAM == \"Chefe\", :].RENDFAMPERCAPITA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--- \n", + "\n", + "
\n", + "\n", + "# QUESTÃO 3\n", + "### Análise do ENEM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--- \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Esta parte da avaliação se baseia em uma amostra de dados do **ENEM 2017** adaptados para esta prova. O dicionario de dados encontra-se no link a seguir:\n", + "\n", + "[Dicionário de dados](./arquivos/DATA_DIC.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 841, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NU_INSCRICAOSG_UF_RESIDENCIANU_IDADETP_SEXOTP_ESTADO_CIVILTP_ST_CONCLUSAOIN_TREINEIRONU_NOTA_CNNU_NOTA_CHNU_NOTA_LC...CARROCELULARFIXOCOMPUTADORINTERNETQ026TIPO_EMRENDANOTA_TESTESNOTA_ENEM
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5 rows × 28 columns

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" + ], + "text/plain": [ + " NU_INSCRICAO SG_UF_RESIDENCIA NU_IDADE TP_SEXO TP_ESTADO_CIVIL \\\n", + "171957 170003469518 MT 26.0 F 0.0 \n", + "127898 170005286919 CE 17.0 M 0.0 \n", + "132925 170005113172 MA 21.0 M 0.0 \n", + "119870 170005346442 PR 17.0 F 0.0 \n", + "122904 170004476042 DF 26.0 F 0.0 \n", + "\n", + " TP_ST_CONCLUSAO IN_TREINEIRO NU_NOTA_CN NU_NOTA_CH NU_NOTA_LC \\\n", + "171957 1 0 605.4 657.4 542.4 \n", + "127898 2 0 698.5 649.0 630.6 \n", + "132925 1 0 591.0 643.8 597.9 \n", + "119870 2 0 525.6 441.2 509.4 \n", + "122904 1 0 538.0 408.8 523.9 \n", + "\n", + " ... CARRO CELULAR FIXO COMPUTADOR INTERNET Q026 TIPO_EM \\\n", + "171957 ... A E A B A A C \n", + "127898 ... C B A B B B D \n", + "132925 ... A D A B B A A \n", + "119870 ... B C A B B B A \n", + "122904 ... B C A A A A A \n", + "\n", + " RENDA NOTA_TESTES NOTA_ENEM \n", + "171957 3020.091575 2430.9 2990.9 \n", + "127898 3611.589780 2817.1 3677.1 \n", + "132925 1375.454182 2385.8 3325.8 \n", + "119870 2079.516469 1930.0 2450.0 \n", + "122904 1364.095119 1921.8 2381.8 \n", + "\n", + "[5 rows x 28 columns]" + ] + }, + "execution_count": 841, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filename = \"arquivos/enem2017_30K.csv\"\n", + "dados = pd.read_csv(filename, index_col=0)\n", + "dados.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**a)** Apresente uma tabela cruzada que mostre a escolaridade do pai cruzada com o tipo do ensino médio. Aponte a combinação de categorias mais frequente e, baseado no dicionário de dados, explique o que são. " + ] + }, + { + "cell_type": "code", + "execution_count": 842, + "metadata": {}, + "outputs": [], + "source": [ + "dados.TIPO_EM = dados.TIPO_EM.astype('category')\n", + "dados.ESC_PAI = dados.ESC_PAI.astype('category')" + ] + }, + { + "cell_type": "code", + "execution_count": 843, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ESC_PAIABCDEFGH
TIPO_EM
A5.03333320.52333313.22333310.45000018.2200003.0333331.1933337.643333
B0.0633330.3933330.3566670.4233331.2200000.4200000.2533330.250000
C0.0566670.2600000.2066670.1700000.4833330.1133330.0566670.090000
D0.0633330.4366670.6300000.9966674.4666673.3333332.6533330.443333
E0.0466670.2533330.2733330.3200001.0700000.4600000.2566670.160000
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" + ], + "text/plain": [ + "ESC_PAI A B C D E F \\\n", + "TIPO_EM \n", + "A 5.033333 20.523333 13.223333 10.450000 18.220000 3.033333 \n", + "B 0.063333 0.393333 0.356667 0.423333 1.220000 0.420000 \n", + "C 0.056667 0.260000 0.206667 0.170000 0.483333 0.113333 \n", + "D 0.063333 0.436667 0.630000 0.996667 4.466667 3.333333 \n", + "E 0.046667 0.253333 0.273333 0.320000 1.070000 0.460000 \n", + "\n", + "ESC_PAI G H \n", + "TIPO_EM \n", + "A 1.193333 7.643333 \n", + "B 0.253333 0.250000 \n", + "C 0.056667 0.090000 \n", + "D 2.653333 0.443333 \n", + "E 0.256667 0.160000 " + ] + }, + "execution_count": 843, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(dados.TIPO_EM, dados.ESC_PAI, normalize='all')*100" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "A combinação mais frequente é entre alunos que estudarem somente em escola pública e pai que não completou o quinto ano do fundamental." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**b)** Baseado nos dados, encontre a idade da mulher mais idosa que prestou a prova " + ] + }, + { + "cell_type": "code", + "execution_count": 844, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "74.0" + ] + }, + "execution_count": 844, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.loc[dados.TP_SEXO == 'F', :].NU_IDADE.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**c)** Avalie a afirmação: pessoas com mais renda tendem a ir melhor no ENEM \n", + "\n", + "É necessário:\n", + "\n", + "* realizar o plot de dispersão renda versus pontuação" + ] + }, + { + "cell_type": "code", + "execution_count": 845, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "plt.scatter(dados.RENDA, dados.NOTA_ENEM)\n", + "plt.title(\"Renda x Nota ENEM\")\n", + "plt.xlabel(\"Renda\")\n", + "plt.ylabel(\"Nota ENEM\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 846, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.40819007923966083" + ] + }, + "execution_count": 846, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.RENDA.corr(dados.NOTA_ENEM)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "A correlação entre as duas variáveis é moderada, havendo uma grande dispersão das notas em mesmas faixas de renda,\n", + "mas para faixas de renda superiores as notas tendem a ir afunilando para notas maiores " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**d)** Compare as várias categorias do ensino médio. Use um recurso gráfico que permita visualizar a distribuição das notas do ENEM comparando as categorias de ensino médio." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "
\n", + "\n", + "# KAGGLE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O [Kaggle.com](https://www.kaggle.com/competitions) é um site de competições internacionais de Data Science no qual empresas oferecem prêmios para os analistas que fizerem a melhor modelagem preditiva de diversos problemas.\n", + "\n", + "Em uma das competições recente, o desafio era:\n", + "\n", + ">Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n", + "\n", + ">With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n", + "\n", + "A seguir, vocês receberão um conjunto de dados bem menor em termos de número de variáveis disponível no Kaggle.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SIGNIFICADO DAS VARIÁVEIS\n", + "\n", + "A base de dados que terão acesso traz informações de 1460 imóveis cujas variáveis selecionadas possuem as seguintes descrições traduzidas (mantendo os nomes das variáveis e das categorias do problema original - em inglês):\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "✔ **Id:** número que identifica o imóvel na base de dados.\n", + "\n", + "✔ **SalePrice:** preço de venda da propriedade (em dólares). Ess\n", + "a é a variável alvo que você irá tentar predizer.\n", + "\n", + "✔ **Foundation:** tipo de fundação, com as seguintes categorias:\n", + "- BrkTil - Tijolo e telha\n", + "- CBlock - Bloco de concreto\n", + "- PConc - Concreto usinado\n", + "- Slab - Laje\n", + "- Stone - Pedra\n", + "- Wood - Madeira\n", + "\n", + "\n", + "✔ **OverallQual**: avalia de maneira geral o material e o acabamento da casa, com as seguintes categorias: \n", + "- Very Poor - Péssimo\n", + "- Poor - Ruim\n", + "- Fair - Justo\n", + "- Below Average - Abaixo da média\n", + "- Average - Médio\n", + "- Above Average - Acima da média\n", + "- Good - Bom\n", + "- Very Good - Muito bom\n", + "- Excellent - Excelente\n", + "- Very Excellent - Muito excelente\n", + "\n", + "✔ **LotArea:** tamanho do lote (em pés quadrados).\n", + "\n", + "✔ **X1stFlrSF:** tamanho do primeiro andar (em pés quadrados).\n", + "\n", + "✔ **X2ndFlrSF:** tamanho do segundo andar (em pés quadrados).\n", + "\n", + "✔ **GarageCars:** tamanho da garagem em capacidade de carros (número de carros).\n", + "\n", + "✔ **Fireplaces:** número de lareiras.\n", + "\n", + "✔ **Street:** tipo de acesso rodoviário à propriedade, com as seguintes categorias:\n", + "- Grvl - Cascalho\n", + "- Pave - Pavimentado\n", + "\n", + "✔ **LandSlope:** inclinação da propriedade, com as seguintes categorias:\n", + "- Gtl - inclinação suave\n", + "- Mod - inclinação moderada\n", + "- Sev - severo decline\n", + "\n", + "✔ **RoofStyle:** tipo do telhado, com as seguintes categorias:\n", + "- Flat - Telhado plano\n", + "- Gable - Telhado de duas águas\n", + "- Gambrel - Telhado estilo celeiro\n", + "- Hip - Telhado de ripa\n", + "- Mansard - Mansarda\n", + "- Shed - Galpão\n", + "\n", + "✔ **CentralAir:** ar condicionado central, com as seguintes categorias:\n", + "- N - Não\n", + "- Y - Sim\n", + "\n", + "✔ **YearBuilt:** ano da construção do imóvel.\n", + "\n", + "✔ **YrSold:** ano de venda do imóvel.\n", + "\n", + "O arquivo traz todas essas 15 variáveis." + ] + }, + { + "cell_type": "code", + "execution_count": 847, + "metadata": {}, + "outputs": [], + "source": [ + "dados = pd.read_excel(\"arquivos/house.xlsx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 848, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " SalePrice Foundation OverallQual LotArea X1stFlrSF X2ndFlrSF \\\n", + "0 208500 PConc Good 8450 856 854 \n", + "1 181500 CBlock Above Average 9600 1262 0 \n", + "2 223500 PConc Good 11250 920 866 \n", + "3 140000 BrkTil Good 9550 961 756 \n", + "4 250000 PConc Very Good 14260 1145 1053 \n", + "\n", + " GarageCars Fireplaces Street LandSlope RoofStyle CentralAir YearBuilt \\\n", + "0 2 0 Pave Gtl Gable Y 2003 \n", + "1 2 1 Pave Gtl Gable Y 1976 \n", + "2 2 1 Pave Gtl Gable Y 2001 \n", + "3 3 1 Pave Gtl Gable Y 1915 \n", + "4 3 1 Pave Gtl Gable Y 2000 \n", + "\n", + " YrSold \n", + "0 2008 \n", + "1 2007 \n", + "2 2008 \n", + "3 2006 \n", + "4 2008 " + ] + }, + "execution_count": 848, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 849, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SalePrice int64\n", + "Foundation object\n", + "OverallQual object\n", + "LotArea int64\n", + "X1stFlrSF int64\n", + "X2ndFlrSF int64\n", + "GarageCars int64\n", + "Fireplaces int64\n", + "Street object\n", + "LandSlope object\n", + "RoofStyle object\n", + "CentralAir object\n", + "YearBuilt int64\n", + "YrSold int64\n", + "dtype: object" + ] + }, + "execution_count": 849, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 850, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:8: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.Foundation.cat.categories = (['BrkTil','CBlock','PConc','Slab','Stone','Wood'])\n", + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:9: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.OverallQual.cat.categories = (['Very Poor','Poor','Fair','Below Average','Average',\n", + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:12: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.Street.cat.categories = (['Grvl','Pave'])\n", + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:13: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.LandSlope.cat.categories = (['Gtl','Mod','Sev'])\n", + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:14: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.RoofStyle.cat.categories = (['Flat','Gable','Gambrel','Hip','Mansard','Shed'])\n", + "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7692\\2871752144.py:15: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n", + " dados.CentralAir.cat.categories = (['N','Y'])\n" + ] + } + ], + "source": [ + "dados.Foundation = dados.Foundation.astype('category')\n", + "dados.OverallQual = dados.OverallQual.astype('category')\n", + "dados.Street = dados.Street.astype('category')\n", + "dados.LandSlope = dados.LandSlope.astype('category')\n", + "dados.RoofStyle = dados.RoofStyle.astype('category')\n", + "dados.CentralAir = dados.CentralAir.astype('category')\n", + "\n", + "dados.Foundation.cat.categories = (['BrkTil','CBlock','PConc','Slab','Stone','Wood'])\n", + "dados.OverallQual.cat.categories = (['Very Poor','Poor','Fair','Below Average','Average',\n", + " 'Above Average','Good','Very Good','Excellent','Very Excellent'])\n", + "\n", + "dados.Street.cat.categories = (['Grvl','Pave'])\n", + "dados.LandSlope.cat.categories = (['Gtl','Mod','Sev'])\n", + "dados.RoofStyle.cat.categories = (['Flat','Gable','Gambrel','Hip','Mansard','Shed'])\n", + "dados.CentralAir.cat.categories = (['N','Y'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "# Questão 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "#### Assuma que o objetivo para trabalhar com a base de dados acima seja: “Analisar como uma determinada variável pode impactar sobre o preço de vendas de um imóvel”.\n", + "\n", + "**a)** Utilizando alguma ferramenta estatística (**gráfica**) que seja adequada, analise como a presença ou não de ar central no imóvel (variável `CentralAir`) pode impactar sobre preço de vendas de um imóvel (variável `SalePrice`). A interpretação precisa ser completa PORÉM sucinta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "#### Assuma que o objetivo para trabalhar com a base de dados acima seja: “Analisar como uma determinada variável pode impactar sobre o preço de vendas de um imóvel”.\n", + "\n", + "**b)** Utilizando alguma ferramentas estatísticas (**numérica** E **gráfica**) que sejam adequadas, analise como o tamanho de área construída do primeiro andar do imóvel (variável `X1stFlrSF`) pode impactar sobre preço de vendas do mesmo (variável `SalePrice`). A interpretação precisa ser completa PORÉM sucinta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "**c)** Como você verificaria a seguinte afirmação: *Os imóveis sem ar central possuem a mesma qualidade geral de material e de acabamento quando comparados com imóveis que possuem ar central*?\n", + "\n", + "Obs: Considere as variáveis `CentralAir` e `OverallQual` para responder a pergunta acima." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lista1/arquivos/DATA_DIC.pdf b/lista1/arquivos/DATA_DIC.pdf new file mode 100644 index 0000000..51660c7 Binary 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index 0000000..a65e63f --- /dev/null +++ b/lista3/Teoria da Probabilidade Classificador Naive Bayes Gabarito.html @@ -0,0 +1,14082 @@ + + + + +Teoria da Probabilidade Classificador Naive Bayes Gabarito + + + + + + + + + + + + + + + + + + + + + + +
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+

Classificador Naive Bayes

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Carregando algumas bibliotecas:

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In [2]:
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%matplotlib inline
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
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+from scipy import stats
+import math
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+# Para ter melhor print
+from IPython.display import display
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+

Índice

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Questão 1

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O serviço de entregas de comida iQueMedo entrega apenas 4 itens diferentes durante a semana: coxinha (C), carne misteriosa (M), peixe caramelizado - pensa uma maçã do amor, mas de sardinha - (P) e ostra vintage (O). Aos sábados servem pizza ou sushi.

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O DataFrame abaixo mostra o histórico de compras de alguns clientes durante a semana, e qual o pedido que fizeram no sábado. A coluna "Semana" contém uma string onde cada letra representa um pedido (dentre as opções acima: 'C', 'M', 'P' e 'O'), e a coluna "Final" mostra o que foi pedido no final de semana.

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In [26]:
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import os
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+filename = 'comidas.csv'
+if filename in os.listdir():
+    print(f'Encontrei o arquivo {filename}, tudo certo para prosseguir com a prova!')
+else:
+    print(f'Não encontrei o arquivo {filename} aqui no diretório {os.getcwd()}, '
+          'será que você não baixou o arquivo?')
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+
Encontrei o arquivo comidas.csv, tudo certo para prosseguir com a prova!
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In [27]:
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comidas = pd.read_csv(filename, sep=";")
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In [28]:
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comidas.head()
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Out[28]:
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SábadoSemana
0pizzaPMPCPCC
1sushiCPO
2sushiPPMPOPOOPP
3sushiPOPPO
4pizzaCPMM
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a) Queremos um sistema que permita escolher, baseado no histórico de pedidos de um cliente, se ele vai preferir sushi ou pizza no sábado.

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Construa um classificador Naive-Bayes para esse problema. Ou seja, determine:

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  • as probabilidades condicionais de comida por preferência de final de semana, e
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  • as probabilidades a priori das preferências de final de semana.
    • +
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Observação: você deve adotar as seguintes medidas simplificadoras:

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  • Não precisa separar em treinamento e teste, estamos interessados apenas no classificador treinado com o conjunto completo de dados.
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  • Não precisa implementar smoothing.
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  • Não tem "palavra desconhecida": os pedidos são apenas as letras C, M, P e O.
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Dica: para separar uma string em lista de letras é simples: basta usar a função list(). Por exemplo:

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In [29]:
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texto = 'PPMPOPOOPP'
+lista = list(texto)
+print(lista)
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['P', 'P', 'M', 'P', 'O', 'P', 'O', 'O', 'P', 'P']
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In [30]:
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# Coloque seu código aqui...
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In [32]:
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# PROF
+textao_pizza = []
+for item in comidas.loc[comidas['Sábado'] == 'pizza', 'Semana']:
+    textao_pizza += list(item)
+probs_pizza = pd.Series(textao_pizza).value_counts(True)
+
+textao_sushi = []
+for item in comidas.loc[comidas['Sábado'] == 'sushi', 'Semana']:
+    textao_sushi += list(item)
+probs_sushi = pd.Series(textao_sushi).value_counts(True)
+
+ser_prior = comidas['Sábado'].value_counts(True)
+prior_pizza = ser_prior['pizza']
+prior_sushi = ser_prior['sushi']
+
+print('Condicional sushi')
+print(probs_sushi)
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+print('Condicional pizza')
+print(probs_pizza)
+
+print('Prior sushi')
+print(prior_sushi)
+
+print('Prior pizza')
+print(prior_pizza)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Condicional sushi
+P    0.504
+O    0.304
+C    0.104
+M    0.088
+dtype: float64
+Condicional pizza
+C    0.398293
+M    0.294452
+P    0.207681
+O    0.099573
+dtype: float64
+Prior sushi
+0.55
+Prior pizza
+0.45
+
+
+
+ +
+
+ +
+
+
+
+
+

b) Determine a recomendação do sistema para um cliente com o seguinte histórico de pedidos: "PPCPCCCOPP"

+

Observação: não precisa implementar o "truque do log".

+ +
+
+
+
+
+
In [33]:
+
+
+
# Coloque seu código aqui...
+
+ +
+
+
+ +
+
+
+
In [34]:
+
+
+
# PROF
+pedidos = 'PPCPCCCOPP'
+
+p_pizza = prior_pizza
+for letra in pedidos:
+    p_pizza *= probs_pizza[letra]
+    
+p_sushi = prior_sushi
+for letra in pedidos:
+    p_sushi *= probs_sushi[letra]
+    
+if p_pizza > p_sushi:
+    print('Come pizza')
+else:
+    print('Come sushi')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Come sushi
+
+
+
+ +
+
+ +
+
+
+
+
+

c) Saiu uma notícia no jornal que diz que comer peixe caramelizado aumenta muito a incidência de unha encravada, daquelas bem horríveis. (Caso você goste de unha encravada: suponha que a população em geral não gosta.)

+

Comente o que você espera que aconteça se você continuar usando seu classificador Naive-Bayes para fazer as recomendações de pizza ou sushi.

+

(Nesta questão não tem código ou cálculos para fazer - apenas senso crítico.)

+ +
+
+
+
+
+
In [34]:
+
+
+
# Coloque sua resposta aqui...
+
+ +
+
+
+ +
+
+
+
+

O classificador naive-Bayes, se usado como está, apresenta recomendações de pizza ou sushi baseado no gosto antigo da população, onde peixe caramelizado era mais popular. Com isso, as recomendações oferecidas pelo classificador nesta nova situação não serão ideais, havendo um excesso de recomendações errôneas. É necessário coletar novos dados e retreinar o classificador (ou seja, recalcular os priors e as condicionais).

+ +
+
+
+
+
+
+
+

Questão 2 - análise de texto

+
+
+
+
+
+
+
+ +
+
+
+
+
+
+

Fonte: https://commons.wikimedia.org/wiki/File:CocaColaBottle_background_free.png

+ +
+
+
+
+
+
+

Esta questão é baseada no Projeto 2

+ +
+
+
+
+
+
+

Você foi contratado pela empresa The Coca Cola Company para fazer monitoramento de redes sociais. Sua empresa fez um bot que continuamente busca por novas postagens no Twitter e as classifica.

+ +
+
+
+
+
+
+

A empresa está interessada em saber quais mensagens estão felizes com a marca versus o total que estão neutros ou insatisfeitos.

+ +
+
+
+
+
+
+

Sua base de treinamento está no arquivo coca_cola.xlsx.

+

As mensagens felizes têm a coluna Categoria com valor FELIZ e as mensagens que não são felizes têm como Categoria o valor NAO_FELIZ.

+ +
+
+
+
+
+
+

Note que a coluna Texto já passou pelas seguintes etapas:

+
    +
  • Conversão para letras minúsculas
    • +
  • Limpeza de pontuação
    • +
+ +
+
+
+
+
+
In [31]:
+
+
+
tweets = pd.read_excel("Q3_coca_cola.xlsx")
+
+ +
+
+
+ +
+
+
+
In [32]:
+
+
+
tweets.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[32]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TextoCategoria
0acho que comer hamburguer de soja e se entupir...NAO_FELIZ
1gosto mais de vaca preta coca cola com sorvet...FELIZ
2me prometo uma coca cola gelada para o fim do...FELIZ
3essa coca cola gelada fez meu diaFELIZ
4prometo parar com doce refrigerante coca cola ...NAO_FELIZ
+
+
+ +
+ +
+
+ +
+
+
+
+

Pede-se:

+ +
+
+
+
+
+
+
+

a) Junte todas as mensagens da cada categoria em uma string única

+ +
+
+
+
+
+
In [33]:
+
+
+
feliz = tweets[tweets.Categoria=="FELIZ"]
+feliz_texto = " ".join(feliz.Texto)
+feliz_series=pd.Series(feliz_texto.split())
+
+
+nao_feliz = tweets[tweets.Categoria=="NAO_FELIZ"]
+nao_feliz_texto = " ".join(nao_feliz.Texto)
+nao_feliz_series = pd.Series(nao_feliz_texto.split())
+
+ +
+
+
+ +
+
+
+
+
+

b) Apresente as tabelas que são equivalentes a $P(palavra|FELIZ)$ e $P(palavra|NAO\_FELIZ)$, e que contém todas as palavras presentes nas respectivas categorias.

+

Você deve guardá-las em variáveis e exibi-las de forma semelhante ao que foi feito com o comando tweets.head() acima.

+ +
+
+
+
+
+
+

R.:

+

$P(palavra|FELIZ)$

+ +
+
+
+
+
+
In [34]:
+
+
+
# insira aqui os comandos para obter
+feliz_freq = feliz_series.value_counts(True)
+
+ +
+
+
+ +
+
+
+
+

$P(palavra|NAO\_FELIZ)$

+ +
+
+
+
+
+
In [35]:
+
+
+
# insira aqui os comandos para palavra não feliz. Crie mais células se precisar
+nao_feliz_freq = nao_feliz_series.value_counts(True)
+
+ +
+
+
+ +
+
+
+
+
+

c) Considerando que:

+
    +
  • Não deve ser feito nenhum tratamento para o caso de palavras inéditas
    • +
  • Não devem ser usadas bibliotecas de machine learning ou estatística
    • +
  • Atenha-se ao pandas e numpy
    • +
+

Por favor faça:

+

Classifique usando um classificador Naive Bayes se a frase "prometo que vai ser a última coca cola do dia" é uma frase que pertence à categoria FELIZ ou NAO_FELIZ

+

Deixe claro (pode ser em comentários) o significado de seus cálculos e a qual conclusão chegou. Principalmente deixei claro em qual número baseou a conclusão

+

Atente que vai ser necessário calcular os valores de $P(FELIZ)$ e $P(NAO\_FELIZ)$

+ +
+
+
+
+
+
In [36]:
+
+
+
frase = "prometo que vai ser a última coca cola do dia" # frase a classificar
+
+ +
+
+
+ +
+
+
+
+

Primeiramente vamos calcular $P(FELIZ)$ e $P(NAO\_FELIZ)$ seguindo a mesma estratégia usada no projeto 2, ou seja, a proporção dos tweets

+ +
+
+
+
+
+
In [48]:
+
+
+
P_feliz = len(feliz)/len(tweets)
+P_nao_feliz = len(nao_feliz)/len(tweets)
+
+ +
+
+
+ +
+
+
+
In [46]:
+
+
+
P_nao_feliz
+
+ +
+
+
+ +
+
+ + +
+ +
Out[46]:
+ + + + +
+
0.4230769230769231
+
+ +
+ +
+
+ +
+
+
+
In [47]:
+
+
+
P_feliz
+
+ +
+
+
+ +
+
+ + +
+ +
Out[47]:
+ + + + +
+
0.5769230769230769
+
+ +
+ +
+
+ +
+
+
+
In [40]:
+
+
+
p_frase_dado_nao_feliz = nao_feliz_freq[frase.split()].prod()
+
+ +
+
+
+ +
+
+
+
In [41]:
+
+
+
p_frase_dado_feliz = feliz_freq[frase.split()].prod()
+
+ +
+
+
+ +
+
+
+
+

Vamos comparar $P(FELIZ|frase) = \frac{P(frase|FELIZ)P(FELIZ)}{P(frase)}$ com $P(NAO\_FELIZ|frase) = \frac{P(frase|NAO\_FELIZ)P(NAO\_FELIZ)}{P(frase)}$ . Como foi feito na aula de texto com Bayes e no Projeto 2, podemos desprezar o denominador $P(frase)$ que aparece em ambas as fórmulas

+ +
+
+
+
+
+
+

Vamos comparar $P(frase|FELIZ)P(FELIZ)$

+ +
+
+
+
+
+
In [42]:
+
+
+
P_feliz*p_frase_dado_feliz
+
+ +
+
+
+ +
+
+ + +
+ +
Out[42]:
+ + + + +
+
8.840985947103174e-19
+
+ +
+ +
+
+ +
+
+
+
+

Com $P(frase|NAO\_FELIZ)P(NAO\_FELIZ)$

+ +
+
+
+
+
+
In [43]:
+
+
+
P_nao_feliz*p_frase_dado_nao_feliz
+
+ +
+
+
+ +
+
+ + +
+ +
Out[43]:
+ + + + +
+
1.4765785305458623e-18
+
+ +
+ +
+
+ +
+
+
+
+

Como se pode ver, as probabilidades dizem que é mais provável que a frase acima seja NAO_FELIZ

+ +
+
+
+
+
+
+

Para saber quantas vezes P(NAO_FELIZ|frase) é maior vamos fazer $P(NAO\_FELIZ|frase)/P(FELIZ|frase)$

+ +
+
+
+
+
+
In [45]:
+
+
+
(P_nao_feliz*p_frase_dado_nao_feliz)/(P_feliz*p_frase_dado_feliz)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[45]:
+ + + + +
+
1.6701514281104315
+
+ +
+ +
+
+ +
+
+
+
+

Ou seja, a frase é $1.67$ vezes mais provável de ser do tipo NAO_FELIZ que FELIZ

+ +
+
+
+
+
+ + + + + + diff --git a/lista3/Teoria da Probabilidade Classificador Naive Bayes.ipynb b/lista3/Teoria da Probabilidade Classificador Naive Bayes.ipynb new file mode 100644 index 0000000..834abd6 --- /dev/null +++ b/lista3/Teoria da Probabilidade Classificador Naive Bayes.ipynb @@ -0,0 +1,673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classificador Naive Bayes\n", + "\n", + "___\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "Carregando algumas bibliotecas:" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from scipy import stats\n", + "import math\n", + "\n", + "# Para ter melhor print\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "## Índice\n", + "\n", + "- [Questão 1](#q1)\n", + " - [Item a](#q1_a)\n", + " - [Item b](#q1_b)\n", + " - [Item c](#q1_c)\n", + "- [Questão 2](#q2)\n", + " - [Item a](#q2_a)\n", + " - [Item b](#q2_b)\n", + " - [Item c](#q2_c)\n", + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "___\n", + "\n", + "# Questão 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O serviço de entregas de comida iQueMedo entrega apenas 4 itens diferentes durante a semana: coxinha (C), carne misteriosa (M), peixe caramelizado - pensa uma maçã do amor, mas de sardinha - (P) e ostra *vintage* (O). Aos sábados servem pizza ou sushi.\n", + "\n", + "O *DataFrame* abaixo mostra o histórico de compras de alguns clientes durante a semana, e qual o pedido que fizeram no sábado. A coluna \"Semana\" contém uma *string* onde cada letra representa um pedido (dentre as opções acima: 'C', 'M', 'P' e 'O'), e a coluna \"Final\" mostra o que foi pedido no final de semana." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encontrei o arquivo comidas.csv, tudo certo para prosseguir com a prova!\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filename = 'comidas.csv'\n", + "if filename in os.listdir():\n", + " print(f'Encontrei o arquivo {filename}, tudo certo para prosseguir com a prova!')\n", + "else:\n", + " print(f'Não encontrei o arquivo {filename} aqui no diretório {os.getcwd()}, '\n", + " 'será que você não baixou o arquivo?')" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "comidas = pd.read_csv(filename, sep=\";\")" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SábadoSemana
0pizzaPMPCPCC
1sushiCPO
2sushiPPMPOPOOPP
3sushiPOPPO
4pizzaCPMM
\n", + "
" + ], + "text/plain": [ + " Sábado Semana\n", + "0 pizza PMPCPCC\n", + "1 sushi CPO\n", + "2 sushi PPMPOPOOPP\n", + "3 sushi POPPO\n", + "4 pizza CPMM" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comidas.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**a)** Queremos um sistema que permita escolher, baseado no histórico de pedidos de um cliente, se ele vai preferir sushi ou pizza no sábado.\n", + "\n", + "Construa um classificador Naive-Bayes para esse problema. Ou seja, determine:\n", + "\n", + "- as probabilidades condicionais de comida por preferência de final de semana, e \n", + "- as probabilidades *a priori* das preferências de final de semana.\n", + "\n", + "Observação: você deve adotar as seguintes medidas simplificadoras:\n", + "\n", + "- Não precisa separar em treinamento e teste, estamos interessados apenas no classificador treinado com o conjunto completo de dados.\n", + "- Não precisa implementar *smoothing*.\n", + "- Não tem \"palavra desconhecida\": os pedidos são apenas as letras C, M, P e O.\n", + "\n", + "Dica: para separar uma *string* em lista de letras é simples: basta usar a função `list()`. Por exemplo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['P', 'P', 'M', 'P', 'O', 'P', 'O', 'O', 'P', 'P']\n" + ] + } + ], + "source": [ + "texto = 'PPMPOPOOPP'\n", + "lista = list(texto)\n", + "print(lista)" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "# Coloque seu código aqui...\n", + "P_sushi = comidas.Sábado.value_counts(True)['sushi']\n", + "P_pizza = comidas.Sábado.value_counts(True)['pizza']" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "def listac (txt):\n", + " return list(txt)\n", + "\n", + "comidas.Semana = comidas.Semana.apply(listac)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "textao_pizza = []\n", + "for item in comidas.loc[comidas['Sábado'] == 'pizza', 'Semana']:\n", + " textao_pizza += list(item)\n", + "probs_pizza = pd.Series(textao_pizza).value_counts(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [], + "source": [ + "textao_sushi = []\n", + "for item in comidas.loc[comidas['Sábado'] == 'sushi', 'Semana']:\n", + " textao_sushi += list(item)\n", + "probs_sushi = pd.Series(textao_sushi).value_counts(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--- \n", + "\n", + "
\n", + "\n", + "\n", + "**b)** Determine a recomendação do sistema para um cliente com o seguinte histórico de pedidos: \"PPCPCCCOPP\"\n", + "\n", + "Observação: não precisa implementar o \"truque do log\"." + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.356660397139946e-07 6.360954843505357e-07\n" + ] + } + ], + "source": [ + "# Coloque seu código aqui...\n", + "pedido = list('PPCPCCCOPP')\n", + "P_prever_pizza = 1\n", + "for i in pedido:\n", + " P_prever_pizza *= probs_pizza[i]\n", + "P_prever_pizza *= P_pizza\n", + "\n", + "P_prever_sushi = 1\n", + "for i in pedido:\n", + " P_prever_sushi *= probs_sushi[i]\n", + "P_prever_sushi *= P_sushi\n", + "\n", + "print(P_prever_pizza, P_prever_sushi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**c)** Saiu uma notícia no jornal que diz que comer peixe caramelizado aumenta muito a incidência de unha encravada, daquelas bem horríveis. (Caso você goste de unha encravada: suponha que a população em geral não gosta.)\n", + "\n", + "Comente o que você espera que aconteça se você continuar usando seu classificador Naive-Bayes para fazer as recomendações de pizza ou sushi.\n", + "\n", + "(Nesta questão não tem código ou cálculos para fazer - apenas senso crítico.)" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "# Coloque sua resposta aqui...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "
\n", + "\n", + "# Questão 2 - análise de texto" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Fonte: https://commons.wikimedia.org/wiki/File:CocaColaBottle_background_free.png](img/160px-CocaColaBottle_background_Free.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Esta questão é baseada no Projeto 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você foi contratado pela empresa *The Coca Cola Company* para fazer monitoramento de redes sociais. Sua empresa fez um *bot* que continuamente busca por novas postagens no Twitter e as classifica." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A empresa está interessada em saber quais mensagens estão felizes com a marca versus o total que estão neutros ou insatisfeitos." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sua base de treinamento está no arquivo `coca_cola.xlsx`. \n", + "\n", + "As mensagens felizes têm a coluna **Categoria** com valor `FELIZ` e as mensagens que não são felizes têm como **Categoria** o valor `NAO_FELIZ`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note que a coluna Texto já passou pelas seguintes etapas:\n", + "* Conversão para letras minúsculas\n", + "* Limpeza de pontuação" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "tweets = pd.read_excel(\"Q3_coca_cola.xlsx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TextoCategoria
0acho que comer hamburguer de soja e se entupir...NAO_FELIZ
1gosto mais de vaca preta coca cola com sorvet...FELIZ
2me prometo uma coca cola gelada para o fim do...FELIZ
3essa coca cola gelada fez meu diaFELIZ
4prometo parar com doce refrigerante coca cola ...NAO_FELIZ
\n", + "
" + ], + "text/plain": [ + " Texto Categoria\n", + "0 acho que comer hamburguer de soja e se entupir... NAO_FELIZ\n", + "1 gosto mais de vaca preta coca cola com sorvet... FELIZ\n", + "2 me prometo uma coca cola gelada para o fim do... FELIZ\n", + "3 essa coca cola gelada fez meu dia FELIZ\n", + "4 prometo parar com doce refrigerante coca cola ... NAO_FELIZ" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tweets.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pede-se:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**a)** Junte todas as mensagens da cada categoria em uma *string* única\n" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "857\n" + ] + } + ], + "source": [ + "feliz = tweets[tweets.Categoria==\"FELIZ\"]\n", + "feliz_texto = \" \".join(feliz.Texto)\n", + "feliz_series=pd.Series(feliz_texto.split())" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "858\n" + ] + } + ], + "source": [ + "msg_feliz = ''\n", + "for i in tweets.loc[tweets.Categoria == \"FELIZ\", :].Texto:\n", + " msg_feliz += \" \" + i\n", + "\n", + "msg_nao_feliz = ''\n", + "for i in tweets.loc[tweets.Categoria == \"NAO_FELIZ\", :].Texto:\n", + " msg_nao_feliz += \" \" + i" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**b)** Apresente as tabelas que são equivalentes a $P(palavra|FELIZ)$ e $P(palavra|NAO\\_FELIZ)$, e que contém todas as palavras presentes nas respectivas categorias.\n", + "\n", + "Você deve guardá-las em variáveis e exibi-las de forma semelhante ao que foi feito com o comando `tweets.head()` acima." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**R.:**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$P(palavra|FELIZ)$" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "# insira aqui os comandos para obter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$P(palavra|NAO\\_FELIZ)$" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "# insira aqui os comandos para palavra não feliz. Crie mais células se precisar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "---\n", + "\n", + "**c)** Considerando que: \n", + "* Não deve ser feito nenhum tratamento para o caso de palavras inéditas\n", + "* Não devem ser usadas bibliotecas de *machine learning* ou estatística\n", + "* Atenha-se ao `pandas` e `numpy`\n", + "\n", + "\n", + "\n", + "Por favor faça:\n", + "\n", + "Classifique usando um classificador *Naive Bayes* se a frase `\"prometo que vai ser a última coca cola do dia\"` é uma frase que pertence à categoria `FELIZ` ou `NAO_FELIZ`\n", + "\n", + "Deixe claro (pode ser em comentários) o significado de seus cálculos e a qual conclusão chegou. Principalmente deixei claro *em qual número* baseou a conclusão \n", + "\n", + "Atente que vai ser necessário calcular os valores de $P(FELIZ)$ e $P(NAO\\_FELIZ)$\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "frase = \"prometo que vai ser a última coca cola do dia\" # frase a classificar" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lista3/comidas.csv b/lista3/comidas.csv new file mode 100644 index 0000000..ca913fc --- /dev/null +++ b/lista3/comidas.csv @@ -0,0 +1,201 @@ +Sábado;Semana +pizza;PMPCPCC +sushi;CPO +sushi;PPMPOPOOPP +sushi;POPPO +pizza;CPMM +pizza;CPCP +pizza;MCMCPMCP +sushi;POPMPOPO +sushi;OPOMPMOPMPPCO +sushi;OPOPOPPMOP +pizza;CMOMCOMCCP +sushi;MCPPOOCOMO +sushi;OPOPPOPPPOPMO +pizza;MMPPCCCPC +pizza;MPPCPCPCC +pizza;PCCPP +pizza;MMMCCCO +sushi;OMPM +sushi;PPOPPPOP +pizza;MMMOCMMMCPOM +sushi;OPMPPP +pizza;MOPCMMMPOO +pizza;MCOPCOPMMCCCPC +pizza;CCMOCCPMCMC +sushi;CMOPOPPPPPP +sushi;CPPOOOOPPO +pizza;CMMOCM +sushi;PPMPPO +sushi;PPOM +pizza;MCMCO +sushi;PCOOPOPO +pizza;CMCOMM +pizza;CPCPCCC +sushi;OOCOOP +sushi;OPOMOPCPC +sushi;PPPPPO +pizza;MMCPMMMM +pizza;MCCPOCCM +sushi;COOPPOM +sushi;CPOCPOOP +pizza;MCCPMCC +sushi;COPMCOPOOPP +pizza;CMCCPC +sushi;PPPOPOPPPP +pizza;MCCCPPPCMCC +sushi;OPPMPPPO +pizza;PCCCC +sushi;POPPCPP +sushi;PCMPOPPMP +pizza;CMMP +sushi;PPPCCPCMPOP +sushi;PPOPCPM +sushi;PPOPOO +sushi;MPPPP +sushi;OOPPOO +sushi;OCPCPCPPMPPP +pizza;PCCCMCM +pizza;MMMCOPCP +pizza;MPPMCCM +pizza;MCCPOM +pizza;CCCOCMC +pizza;MMPCCCOPMMM +sushi;MPOCCPOP +pizza;COMCM +pizza;CCPCM +sushi;PPPCPOP +pizza;CCCCP +sushi;OOMOCPMCPP +pizza;CMMMOPCCCM +sushi;PPCMPPOPOPP +sushi;PPPOPOO +pizza;CMMCPCCPM +pizza;MMCPPMCMCC +sushi;OOPPPPP +sushi;OPPMPMOPMCP +sushi;PPCPPPPP +sushi;POPPMPCP +pizza;OCOMCMPPP +pizza;MMPCMCOOCP +pizza;OCMOCMCOO +sushi;OPCCPMCOP +sushi;POMOPOPOOP +pizza;OMPPCPC +pizza;MCCCM +pizza;CPCCMP +pizza;CMPMCCP +sushi;OPPPP +sushi;MPPO +sushi;OPPPMCPPOP +sushi;PPMPPPPO +pizza;CPMOPOCPMM +sushi;OPPPPPC +sushi;PPPOOOP +sushi;OMOMOP +sushi;OOMPOPOPPPP +sushi;OPPPPOOPOO +sushi;POPM +sushi;OPOOC +pizza;MPOPMMP +pizza;CCCCCC +pizza;CCOMC +sushi;OPPPOCP +pizza;MCOPCMPOM +sushi;POPMPPOPP +sushi;OCOPOPC +pizza;MCOCOM +sushi;OOOMOOPOO +sushi;PCPCPPCPMOO +pizza;MMCCCCPC +pizza;COPOOCCPMCPC +pizza;CMMMCPCCP +pizza;OPPMMCMCC +sushi;COPPCPOOCC +sushi;PPPPOOPO +sushi;PPPPO +sushi;PPPM +sushi;OPPPPCPPOO +pizza;PMCCMCPMMMPC +sushi;OPPMOPP +sushi;PMPC +sushi;OPCPCPOCO +sushi;CPOPMPPO +pizza;MMCPPMP +pizza;PPPMMMPCPPCP +pizza;CCPCP +sushi;POOPOPOCPPP +sushi;POCMOPOO +sushi;OOPMOPC +pizza;PMCCMOMCMMPCP +sushi;POPPPPPPCOPOO +sushi;PPPOPPPPMP +pizza;MOCMOCOMC +pizza;CPCMCMCPOOPC +pizza;PCCCMPM +sushi;OPPPOMO +pizza;MCOM +sushi;PPCMPPOCPPC +sushi;MPPPPPCP +pizza;MCPMCCMMC +sushi;OPPPOOPO +sushi;MCOOMPCP +pizza;OCMOMMCCOMP +sushi;OPPPC +pizza;CCCMCPMOC +pizza;PPPPP +pizza;MCCOMP +sushi;PPOOCO +sushi;OOPPPCP +pizza;MCCMMCMC +pizza;MCPCMC +sushi;PPOPCOOOOPOC +pizza;CCMMCM +pizza;PPCMCCOC +sushi;PPM +sushi;MPOPMOPP +pizza;MMPMCCMCC +sushi;PPPOPPOO +sushi;CMCPPMOP +pizza;CCCCMMPC +sushi;PPPCPOPP +pizza;CCMPCO +sushi;PCMPPCO +sushi;OOOPOMOOOOPP +sushi;PPCPOMOOO +pizza;CMPPPPCP +sushi;OPOOM +pizza;CPMPMM +pizza;MPCCCC +pizza;PCOCCPMMM +sushi;POPPPMPOP +sushi;CCPOPPPPO +pizza;PCOOMCMC +sushi;OCPCPPMPP +pizza;CCMPPPMCC +sushi;OOCOPPOO +pizza;OCPMM +sushi;OPPOP +pizza;PMPOMCM +sushi;PPPPMOMP +pizza;OCMMPCPOCC +pizza;OCPMCPMMC +pizza;MCCOCMCP +sushi;PCCMOP +sushi;OPPPPO +pizza;COMCCCM +sushi;PPOOOP +sushi;OPPPPMPOP +sushi;CCOPPOOPP +sushi;OPMOPPP +pizza;CPMCMP +pizza;CCCPMM +sushi;CMOCPPCO +sushi;PPPPCOOP +sushi;POOPPPOOMP +pizza;PPMPCCCCMO +pizza;CCMOCMCCOO +sushi;OOOOPPPC +sushi;POMOPO +sushi;CPPPOOM +sushi;PPPOOPOPOP diff --git a/lista3/img/160px-CocaColaBottle_background_free.png b/lista3/img/160px-CocaColaBottle_background_free.png new file mode 100644 index 0000000..f0c20f8 Binary files /dev/null and b/lista3/img/160px-CocaColaBottle_background_free.png differ