├── Certificate
    └── certificado-jornada-python.png
├── Python Dev
    └── codigo.py
├── Python IA
    ├── clientes.csv
    ├── codigo.ipynb
    └── novos_clientes.csv
├── Python Insights
    ├── cancelamentos_sample.csv
    └── codigo.ipynb
├── Python PowerUp
    ├── codigo.py
    ├── pegar_posicao.py
    └── produtos.csv
└── README.md


/Certificate/certificado-jornada-python.png:
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https://raw.githubusercontent.com/paulo-magls/curso-python/fc82d00be5308180b5c21f6ecccd063fa5c268a3/Certificate/certificado-jornada-python.png


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/Python Dev/codigo.py:
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 1 | # Hashzap
 2 | # botao de iniciar chat
 3 | # popup para entrar no chat
 4 | # quando entrar no chat: (aparece para todo mundo)
 5 |     # a mensagem que você entrou no chat
 6 |     # o campo e o botão de enviar mensagem
 7 | # a cada mensagem que você envia (aparece para todo mundo)
 8 |     # Nome: Texto da Mensagem
 9 | 
10 | import flet as ft
11 | 
12 | def main(pagina):
13 |     texto = ft.Text("Hashzap")
14 | 
15 |     chat = ft.Column()
16 | 
17 |     nome_usuario = ft.TextField(label="Escreva seu nome")
18 | 
19 |     def enviar_mensagem_tunel(mensagem):
20 |         tipo = mensagem["tipo"]
21 |         if tipo == "mensagem":
22 |             texto_mensagem = mensagem["texto"]
23 |             usuario_mensagem = mensagem["usuario"]
24 |             # adicionar a mensagem no chat
25 |             chat.controls.append(ft.Text(f"{usuario_mensagem}: {texto_mensagem}"))
26 |         else:
27 |             usuario_mensagem = mensagem["usuario"]
28 |             chat.controls.append(ft.Text(f"{usuario_mensagem} entrou no chat", 
29 |                                          size=12, italic=True, color=ft.colors.ORANGE_500))
30 |         pagina.update()
31 | 
32 |     pagina.pubsub.subscribe(enviar_mensagem_tunel)
33 | 
34 |     def enviar_mensagem(evento):
35 |         pagina.pubsub.send_all({"texto": campo_mensagem.value, "usuario": nome_usuario.value,
36 |                                 "tipo": "mensagem"})
37 |         # limpar o campo de mensagem
38 |         campo_mensagem.value = ""
39 |         pagina.update()
40 | 
41 |     campo_mensagem = ft.TextField(label="Digite uma mensagem", on_submit=enviar_mensagem)
42 |     botao_enviar_mensagem = ft.ElevatedButton("Enviar", on_click=enviar_mensagem)
43 | 
44 |     def entrar_popup(evento):
45 |         pagina.pubsub.send_all({"usuario": nome_usuario.value, "tipo": "entrada"})
46 |         # adicionar o chat
47 |         pagina.add(chat)
48 |         # fechar o popup
49 |         popup.open = False
50 |         # remover o botao iniciar chat
51 |         pagina.remove(botao_iniciar)
52 |         pagina.remove(texto)
53 |         # criar o campo de mensagem do usuario
54 |         # criar o botao de enviar mensagem do usuario
55 |         pagina.add(ft.Row(
56 |             [campo_mensagem, botao_enviar_mensagem]
57 |         ))
58 |         pagina.update()
59 | 
60 |     popup = ft.AlertDialog(
61 |         open=False, 
62 |         modal=True,
63 |         title=ft.Text("Bem vindo ao Hashzap"),
64 |         content=nome_usuario,
65 |         actions=[ft.ElevatedButton("Entrar", on_click=entrar_popup)],
66 |         )
67 | 
68 |     def entrar_chat(evento):
69 |         pagina.dialog = popup
70 |         popup.open = True
71 |         pagina.update()
72 | 
73 |     botao_iniciar = ft.ElevatedButton("Iniciar chat", on_click=entrar_chat)
74 | 
75 |     pagina.add(texto)
76 |     pagina.add(botao_iniciar)
77 | 
78 | ft.app(target=main, view=ft.WEB_BROWSER, port=8000)
79 | 
80 | # deploy


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/Python IA/codigo.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Projeto Python IA: Inteligência Artificial e Previsões\n",
  8 |     "\n",
  9 |     "### Case: Score de Crédito dos Clientes\n",
 10 |     "\n",
 11 |     "Você foi contratado por um banco para conseguir definir o score de crédito dos clientes. Você precisa analisar todos os clientes do banco e, com base nessa análise, criar um modelo que consiga ler as informações do cliente e dizer automaticamente o score de crédito dele: Ruim, Ok, Bom\n",
 12 |     "\n",
 13 |     "Arquivos da aula: https://drive.google.com/drive/folders/1FbDqVq4XLvU85VBlVIMJ73p9oOu6u2-J?usp=drive_link"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 10,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "data": {
 23 |       "text/html": [
 24 |        "<div>\n",
 25 |        "<style scoped>\n",
 26 |        "    .dataframe tbody tr th:only-of-type {\n",
 27 |        "        vertical-align: middle;\n",
 28 |        "    }\n",
 29 |        "\n",
 30 |        "    .dataframe tbody tr th {\n",
 31 |        "        vertical-align: top;\n",
 32 |        "    }\n",
 33 |        "\n",
 34 |        "    .dataframe thead th {\n",
 35 |        "        text-align: right;\n",
 36 |        "    }\n",
 37 |        "</style>\n",
 38 |        "<table border=\"1\" class=\"dataframe\">\n",
 39 |        "  <thead>\n",
 40 |        "    <tr style=\"text-align: right;\">\n",
 41 |        "      <th></th>\n",
 42 |        "      <th>id_cliente</th>\n",
 43 |        "      <th>mes</th>\n",
 44 |        "      <th>idade</th>\n",
 45 |        "      <th>profissao</th>\n",
 46 |        "      <th>salario_anual</th>\n",
 47 |        "      <th>num_contas</th>\n",
 48 |        "      <th>num_cartoes</th>\n",
 49 |        "      <th>juros_emprestimo</th>\n",
 50 |        "      <th>num_emprestimos</th>\n",
 51 |        "      <th>dias_atraso</th>\n",
 52 |        "      <th>...</th>\n",
 53 |        "      <th>idade_historico_credito</th>\n",
 54 |        "      <th>investimento_mensal</th>\n",
 55 |        "      <th>comportamento_pagamento</th>\n",
 56 |        "      <th>saldo_final_mes</th>\n",
 57 |        "      <th>score_credito</th>\n",
 58 |        "      <th>emprestimo_carro</th>\n",
 59 |        "      <th>emprestimo_casa</th>\n",
 60 |        "      <th>emprestimo_pessoal</th>\n",
 61 |        "      <th>emprestimo_credito</th>\n",
 62 |        "      <th>emprestimo_estudantil</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>3392</td>\n",
 69 |        "      <td>1</td>\n",
 70 |        "      <td>23.0</td>\n",
 71 |        "      <td>cientista</td>\n",
 72 |        "      <td>19114.12</td>\n",
 73 |        "      <td>3.0</td>\n",
 74 |        "      <td>4.0</td>\n",
 75 |        "      <td>3.0</td>\n",
 76 |        "      <td>4.0</td>\n",
 77 |        "      <td>3.0</td>\n",
 78 |        "      <td>...</td>\n",
 79 |        "      <td>265.0</td>\n",
 80 |        "      <td>21.465380</td>\n",
 81 |        "      <td>alto_gasto_pagamento_baixos</td>\n",
 82 |        "      <td>312.494089</td>\n",
 83 |        "      <td>Good</td>\n",
 84 |        "      <td>1</td>\n",
 85 |        "      <td>1</td>\n",
 86 |        "      <td>1</td>\n",
 87 |        "      <td>1</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>1</th>\n",
 92 |        "      <td>3392</td>\n",
 93 |        "      <td>2</td>\n",
 94 |        "      <td>23.0</td>\n",
 95 |        "      <td>cientista</td>\n",
 96 |        "      <td>19114.12</td>\n",
 97 |        "      <td>3.0</td>\n",
 98 |        "      <td>4.0</td>\n",
 99 |        "      <td>3.0</td>\n",
100 |        "      <td>4.0</td>\n",
101 |        "      <td>3.0</td>\n",
102 |        "      <td>...</td>\n",
103 |        "      <td>266.0</td>\n",
104 |        "      <td>21.465380</td>\n",
105 |        "      <td>baixo_gasto_pagamento_alto</td>\n",
106 |        "      <td>284.629162</td>\n",
107 |        "      <td>Good</td>\n",
108 |        "      <td>1</td>\n",
109 |        "      <td>1</td>\n",
110 |        "      <td>1</td>\n",
111 |        "      <td>1</td>\n",
112 |        "      <td>0</td>\n",
113 |        "    </tr>\n",
114 |        "    <tr>\n",
115 |        "      <th>2</th>\n",
116 |        "      <td>3392</td>\n",
117 |        "      <td>3</td>\n",
118 |        "      <td>23.0</td>\n",
119 |        "      <td>cientista</td>\n",
120 |        "      <td>19114.12</td>\n",
121 |        "      <td>3.0</td>\n",
122 |        "      <td>4.0</td>\n",
123 |        "      <td>3.0</td>\n",
124 |        "      <td>4.0</td>\n",
125 |        "      <td>3.0</td>\n",
126 |        "      <td>...</td>\n",
127 |        "      <td>267.0</td>\n",
128 |        "      <td>21.465380</td>\n",
129 |        "      <td>baixo_gasto_pagamento_medio</td>\n",
130 |        "      <td>331.209863</td>\n",
131 |        "      <td>Good</td>\n",
132 |        "      <td>1</td>\n",
133 |        "      <td>1</td>\n",
134 |        "      <td>1</td>\n",
135 |        "      <td>1</td>\n",
136 |        "      <td>0</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>3</th>\n",
140 |        "      <td>3392</td>\n",
141 |        "      <td>4</td>\n",
142 |        "      <td>23.0</td>\n",
143 |        "      <td>cientista</td>\n",
144 |        "      <td>19114.12</td>\n",
145 |        "      <td>3.0</td>\n",
146 |        "      <td>4.0</td>\n",
147 |        "      <td>3.0</td>\n",
148 |        "      <td>4.0</td>\n",
149 |        "      <td>5.0</td>\n",
150 |        "      <td>...</td>\n",
151 |        "      <td>268.0</td>\n",
152 |        "      <td>21.465380</td>\n",
153 |        "      <td>baixo_gasto_pagamento_baixo</td>\n",
154 |        "      <td>223.451310</td>\n",
155 |        "      <td>Good</td>\n",
156 |        "      <td>1</td>\n",
157 |        "      <td>1</td>\n",
158 |        "      <td>1</td>\n",
159 |        "      <td>1</td>\n",
160 |        "      <td>0</td>\n",
161 |        "    </tr>\n",
162 |        "    <tr>\n",
163 |        "      <th>4</th>\n",
164 |        "      <td>3392</td>\n",
165 |        "      <td>5</td>\n",
166 |        "      <td>23.0</td>\n",
167 |        "      <td>cientista</td>\n",
168 |        "      <td>19114.12</td>\n",
169 |        "      <td>3.0</td>\n",
170 |        "      <td>4.0</td>\n",
171 |        "      <td>3.0</td>\n",
172 |        "      <td>4.0</td>\n",
173 |        "      <td>6.0</td>\n",
174 |        "      <td>...</td>\n",
175 |        "      <td>269.0</td>\n",
176 |        "      <td>21.465380</td>\n",
177 |        "      <td>alto_gasto_pagamento_medio</td>\n",
178 |        "      <td>341.489231</td>\n",
179 |        "      <td>Good</td>\n",
180 |        "      <td>1</td>\n",
181 |        "      <td>1</td>\n",
182 |        "      <td>1</td>\n",
183 |        "      <td>1</td>\n",
184 |        "      <td>0</td>\n",
185 |        "    </tr>\n",
186 |        "    <tr>\n",
187 |        "      <th>...</th>\n",
188 |        "      <td>...</td>\n",
189 |        "      <td>...</td>\n",
190 |        "      <td>...</td>\n",
191 |        "      <td>...</td>\n",
192 |        "      <td>...</td>\n",
193 |        "      <td>...</td>\n",
194 |        "      <td>...</td>\n",
195 |        "      <td>...</td>\n",
196 |        "      <td>...</td>\n",
197 |        "      <td>...</td>\n",
198 |        "      <td>...</td>\n",
199 |        "      <td>...</td>\n",
200 |        "      <td>...</td>\n",
201 |        "      <td>...</td>\n",
202 |        "      <td>...</td>\n",
203 |        "      <td>...</td>\n",
204 |        "      <td>...</td>\n",
205 |        "      <td>...</td>\n",
206 |        "      <td>...</td>\n",
207 |        "      <td>...</td>\n",
208 |        "      <td>...</td>\n",
209 |        "    </tr>\n",
210 |        "    <tr>\n",
211 |        "      <th>99995</th>\n",
212 |        "      <td>37932</td>\n",
213 |        "      <td>4</td>\n",
214 |        "      <td>25.0</td>\n",
215 |        "      <td>mecanico</td>\n",
216 |        "      <td>39628.99</td>\n",
217 |        "      <td>4.0</td>\n",
218 |        "      <td>6.0</td>\n",
219 |        "      <td>7.0</td>\n",
220 |        "      <td>2.0</td>\n",
221 |        "      <td>23.0</td>\n",
222 |        "      <td>...</td>\n",
223 |        "      <td>378.0</td>\n",
224 |        "      <td>24.028477</td>\n",
225 |        "      <td>alto_gasto_pagamento_alto</td>\n",
226 |        "      <td>479.866228</td>\n",
227 |        "      <td>Poor</td>\n",
228 |        "      <td>1</td>\n",
229 |        "      <td>0</td>\n",
230 |        "      <td>0</td>\n",
231 |        "      <td>0</td>\n",
232 |        "      <td>1</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>99996</th>\n",
236 |        "      <td>37932</td>\n",
237 |        "      <td>5</td>\n",
238 |        "      <td>25.0</td>\n",
239 |        "      <td>mecanico</td>\n",
240 |        "      <td>39628.99</td>\n",
241 |        "      <td>4.0</td>\n",
242 |        "      <td>6.0</td>\n",
243 |        "      <td>7.0</td>\n",
244 |        "      <td>2.0</td>\n",
245 |        "      <td>18.0</td>\n",
246 |        "      <td>...</td>\n",
247 |        "      <td>379.0</td>\n",
248 |        "      <td>24.028477</td>\n",
249 |        "      <td>alto_gasto_pagamento_medio</td>\n",
250 |        "      <td>496.651610</td>\n",
251 |        "      <td>Poor</td>\n",
252 |        "      <td>1</td>\n",
253 |        "      <td>0</td>\n",
254 |        "      <td>0</td>\n",
255 |        "      <td>0</td>\n",
256 |        "      <td>1</td>\n",
257 |        "    </tr>\n",
258 |        "    <tr>\n",
259 |        "      <th>99997</th>\n",
260 |        "      <td>37932</td>\n",
261 |        "      <td>6</td>\n",
262 |        "      <td>25.0</td>\n",
263 |        "      <td>mecanico</td>\n",
264 |        "      <td>39628.99</td>\n",
265 |        "      <td>4.0</td>\n",
266 |        "      <td>6.0</td>\n",
267 |        "      <td>7.0</td>\n",
268 |        "      <td>2.0</td>\n",
269 |        "      <td>27.0</td>\n",
270 |        "      <td>...</td>\n",
271 |        "      <td>380.0</td>\n",
272 |        "      <td>24.028477</td>\n",
273 |        "      <td>alto_gasto_pagamento_alto</td>\n",
274 |        "      <td>516.809083</td>\n",
275 |        "      <td>Poor</td>\n",
276 |        "      <td>1</td>\n",
277 |        "      <td>0</td>\n",
278 |        "      <td>0</td>\n",
279 |        "      <td>0</td>\n",
280 |        "      <td>1</td>\n",
281 |        "    </tr>\n",
282 |        "    <tr>\n",
283 |        "      <th>99998</th>\n",
284 |        "      <td>37932</td>\n",
285 |        "      <td>7</td>\n",
286 |        "      <td>25.0</td>\n",
287 |        "      <td>mecanico</td>\n",
288 |        "      <td>39628.99</td>\n",
289 |        "      <td>4.0</td>\n",
290 |        "      <td>6.0</td>\n",
291 |        "      <td>7.0</td>\n",
292 |        "      <td>2.0</td>\n",
293 |        "      <td>20.0</td>\n",
294 |        "      <td>...</td>\n",
295 |        "      <td>381.0</td>\n",
296 |        "      <td>24.028477</td>\n",
297 |        "      <td>baixo_gasto_pagamento_alto</td>\n",
298 |        "      <td>319.164979</td>\n",
299 |        "      <td>Standard</td>\n",
300 |        "      <td>1</td>\n",
301 |        "      <td>0</td>\n",
302 |        "      <td>0</td>\n",
303 |        "      <td>0</td>\n",
304 |        "      <td>1</td>\n",
305 |        "    </tr>\n",
306 |        "    <tr>\n",
307 |        "      <th>99999</th>\n",
308 |        "      <td>37932</td>\n",
309 |        "      <td>8</td>\n",
310 |        "      <td>25.0</td>\n",
311 |        "      <td>mecanico</td>\n",
312 |        "      <td>39628.99</td>\n",
313 |        "      <td>4.0</td>\n",
314 |        "      <td>6.0</td>\n",
315 |        "      <td>7.0</td>\n",
316 |        "      <td>2.0</td>\n",
317 |        "      <td>18.0</td>\n",
318 |        "      <td>...</td>\n",
319 |        "      <td>382.0</td>\n",
320 |        "      <td>24.028477</td>\n",
321 |        "      <td>alto_gasto_pagamento_medio</td>\n",
322 |        "      <td>393.673696</td>\n",
323 |        "      <td>Poor</td>\n",
324 |        "      <td>1</td>\n",
325 |        "      <td>0</td>\n",
326 |        "      <td>0</td>\n",
327 |        "      <td>0</td>\n",
328 |        "      <td>1</td>\n",
329 |        "    </tr>\n",
330 |        "  </tbody>\n",
331 |        "</table>\n",
332 |        "<p>100000 rows × 25 columns</p>\n",
333 |        "</div>"
334 |       ],
335 |       "text/plain": [
336 |        "       id_cliente  mes  idade  profissao  salario_anual  num_contas  \\\n",
337 |        "0            3392    1   23.0  cientista       19114.12         3.0   \n",
338 |        "1            3392    2   23.0  cientista       19114.12         3.0   \n",
339 |        "2            3392    3   23.0  cientista       19114.12         3.0   \n",
340 |        "3            3392    4   23.0  cientista       19114.12         3.0   \n",
341 |        "4            3392    5   23.0  cientista       19114.12         3.0   \n",
342 |        "...           ...  ...    ...        ...            ...         ...   \n",
343 |        "99995       37932    4   25.0   mecanico       39628.99         4.0   \n",
344 |        "99996       37932    5   25.0   mecanico       39628.99         4.0   \n",
345 |        "99997       37932    6   25.0   mecanico       39628.99         4.0   \n",
346 |        "99998       37932    7   25.0   mecanico       39628.99         4.0   \n",
347 |        "99999       37932    8   25.0   mecanico       39628.99         4.0   \n",
348 |        "\n",
349 |        "       num_cartoes  juros_emprestimo  num_emprestimos  dias_atraso  ...  \\\n",
350 |        "0              4.0               3.0              4.0          3.0  ...   \n",
351 |        "1              4.0               3.0              4.0          3.0  ...   \n",
352 |        "2              4.0               3.0              4.0          3.0  ...   \n",
353 |        "3              4.0               3.0              4.0          5.0  ...   \n",
354 |        "4              4.0               3.0              4.0          6.0  ...   \n",
355 |        "...            ...               ...              ...          ...  ...   \n",
356 |        "99995          6.0               7.0              2.0         23.0  ...   \n",
357 |        "99996          6.0               7.0              2.0         18.0  ...   \n",
358 |        "99997          6.0               7.0              2.0         27.0  ...   \n",
359 |        "99998          6.0               7.0              2.0         20.0  ...   \n",
360 |        "99999          6.0               7.0              2.0         18.0  ...   \n",
361 |        "\n",
362 |        "       idade_historico_credito  investimento_mensal  \\\n",
363 |        "0                        265.0            21.465380   \n",
364 |        "1                        266.0            21.465380   \n",
365 |        "2                        267.0            21.465380   \n",
366 |        "3                        268.0            21.465380   \n",
367 |        "4                        269.0            21.465380   \n",
368 |        "...                        ...                  ...   \n",
369 |        "99995                    378.0            24.028477   \n",
370 |        "99996                    379.0            24.028477   \n",
371 |        "99997                    380.0            24.028477   \n",
372 |        "99998                    381.0            24.028477   \n",
373 |        "99999                    382.0            24.028477   \n",
374 |        "\n",
375 |        "           comportamento_pagamento  saldo_final_mes  score_credito  \\\n",
376 |        "0      alto_gasto_pagamento_baixos       312.494089           Good   \n",
377 |        "1       baixo_gasto_pagamento_alto       284.629162           Good   \n",
378 |        "2      baixo_gasto_pagamento_medio       331.209863           Good   \n",
379 |        "3      baixo_gasto_pagamento_baixo       223.451310           Good   \n",
380 |        "4       alto_gasto_pagamento_medio       341.489231           Good   \n",
381 |        "...                            ...              ...            ...   \n",
382 |        "99995    alto_gasto_pagamento_alto       479.866228           Poor   \n",
383 |        "99996   alto_gasto_pagamento_medio       496.651610           Poor   \n",
384 |        "99997    alto_gasto_pagamento_alto       516.809083           Poor   \n",
385 |        "99998   baixo_gasto_pagamento_alto       319.164979       Standard   \n",
386 |        "99999   alto_gasto_pagamento_medio       393.673696           Poor   \n",
387 |        "\n",
388 |        "       emprestimo_carro  emprestimo_casa emprestimo_pessoal  \\\n",
389 |        "0                     1                1                  1   \n",
390 |        "1                     1                1                  1   \n",
391 |        "2                     1                1                  1   \n",
392 |        "3                     1                1                  1   \n",
393 |        "4                     1                1                  1   \n",
394 |        "...                 ...              ...                ...   \n",
395 |        "99995                 1                0                  0   \n",
396 |        "99996                 1                0                  0   \n",
397 |        "99997                 1                0                  0   \n",
398 |        "99998                 1                0                  0   \n",
399 |        "99999                 1                0                  0   \n",
400 |        "\n",
401 |        "       emprestimo_credito emprestimo_estudantil  \n",
402 |        "0                       1                     0  \n",
403 |        "1                       1                     0  \n",
404 |        "2                       1                     0  \n",
405 |        "3                       1                     0  \n",
406 |        "4                       1                     0  \n",
407 |        "...                   ...                   ...  \n",
408 |        "99995                   0                     1  \n",
409 |        "99996                   0                     1  \n",
410 |        "99997                   0                     1  \n",
411 |        "99998                   0                     1  \n",
412 |        "99999                   0                     1  \n",
413 |        "\n",
414 |        "[100000 rows x 25 columns]"
415 |       ]
416 |      },
417 |      "metadata": {},
418 |      "output_type": "display_data"
419 |     }
420 |    ],
421 |    "source": [
422 |     "import pandas as pd\n",
423 |     "\n",
424 |     "tabela = pd.read_csv(\"clientes.csv\") # importa a base de dados\n",
425 |     "display(tabela)"
426 |    ]
427 |   },
428 |   {
429 |    "cell_type": "code",
430 |    "execution_count": 11,
431 |    "metadata": {},
432 |    "outputs": [
433 |     {
434 |      "name": "stdout",
435 |      "output_type": "stream",
436 |      "text": [
437 |       "<class 'pandas.core.frame.DataFrame'>\n",
438 |       "RangeIndex: 100000 entries, 0 to 99999\n",
439 |       "Data columns (total 25 columns):\n",
440 |       " #   Column                    Non-Null Count   Dtype  \n",
441 |       "---  ------                    --------------   -----  \n",
442 |       " 0   id_cliente                100000 non-null  int64  \n",
443 |       " 1   mes                       100000 non-null  int64  \n",
444 |       " 2   idade                     100000 non-null  float64\n",
445 |       " 3   profissao                 100000 non-null  object \n",
446 |       " 4   salario_anual             100000 non-null  float64\n",
447 |       " 5   num_contas                100000 non-null  float64\n",
448 |       " 6   num_cartoes               100000 non-null  float64\n",
449 |       " 7   juros_emprestimo          100000 non-null  float64\n",
450 |       " 8   num_emprestimos           100000 non-null  float64\n",
451 |       " 9   dias_atraso               100000 non-null  float64\n",
452 |       " 10  num_pagamentos_atrasados  100000 non-null  float64\n",
453 |       " 11  num_verificacoes_credito  100000 non-null  float64\n",
454 |       " 12  mix_credito               100000 non-null  object \n",
455 |       " 13  divida_total              100000 non-null  float64\n",
456 |       " 14  taxa_uso_credito          100000 non-null  float64\n",
457 |       " 15  idade_historico_credito   100000 non-null  float64\n",
458 |       " 16  investimento_mensal       100000 non-null  float64\n",
459 |       " 17  comportamento_pagamento   100000 non-null  object \n",
460 |       " 18  saldo_final_mes           100000 non-null  float64\n",
461 |       " 19  score_credito             100000 non-null  object \n",
462 |       " 20  emprestimo_carro          100000 non-null  int64  \n",
463 |       " 21  emprestimo_casa           100000 non-null  int64  \n",
464 |       " 22  emprestimo_pessoal        100000 non-null  int64  \n",
465 |       " 23  emprestimo_credito        100000 non-null  int64  \n",
466 |       " 24  emprestimo_estudantil     100000 non-null  int64  \n",
467 |       "dtypes: float64(14), int64(7), object(4)\n",
468 |       "memory usage: 19.1+ MB\n",
469 |       "None\n",
470 |       "Index(['id_cliente', 'mes', 'idade', 'profissao', 'salario_anual',\n",
471 |       "       'num_contas', 'num_cartoes', 'juros_emprestimo', 'num_emprestimos',\n",
472 |       "       'dias_atraso', 'num_pagamentos_atrasados', 'num_verificacoes_credito',\n",
473 |       "       'mix_credito', 'divida_total', 'taxa_uso_credito',\n",
474 |       "       'idade_historico_credito', 'investimento_mensal',\n",
475 |       "       'comportamento_pagamento', 'saldo_final_mes', 'score_credito',\n",
476 |       "       'emprestimo_carro', 'emprestimo_casa', 'emprestimo_pessoal',\n",
477 |       "       'emprestimo_credito', 'emprestimo_estudantil'],\n",
478 |       "      dtype='object')\n"
479 |      ]
480 |     }
481 |    ],
482 |    "source": [
483 |     "# verificar se temos valores vazios ou valores reconhecidos em formato errado\n",
484 |     "print(tabela.info())\n",
485 |     "print(tabela.columns)"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": 12,
491 |    "metadata": {},
492 |    "outputs": [
493 |     {
494 |      "name": "stdout",
495 |      "output_type": "stream",
496 |      "text": [
497 |       "<class 'pandas.core.frame.DataFrame'>\n",
498 |       "RangeIndex: 100000 entries, 0 to 99999\n",
499 |       "Data columns (total 25 columns):\n",
500 |       " #   Column                    Non-Null Count   Dtype  \n",
501 |       "---  ------                    --------------   -----  \n",
502 |       " 0   id_cliente                100000 non-null  int64  \n",
503 |       " 1   mes                       100000 non-null  int64  \n",
504 |       " 2   idade                     100000 non-null  float64\n",
505 |       " 3   profissao                 100000 non-null  int32  \n",
506 |       " 4   salario_anual             100000 non-null  float64\n",
507 |       " 5   num_contas                100000 non-null  float64\n",
508 |       " 6   num_cartoes               100000 non-null  float64\n",
509 |       " 7   juros_emprestimo          100000 non-null  float64\n",
510 |       " 8   num_emprestimos           100000 non-null  float64\n",
511 |       " 9   dias_atraso               100000 non-null  float64\n",
512 |       " 10  num_pagamentos_atrasados  100000 non-null  float64\n",
513 |       " 11  num_verificacoes_credito  100000 non-null  float64\n",
514 |       " 12  mix_credito               100000 non-null  int32  \n",
515 |       " 13  divida_total              100000 non-null  float64\n",
516 |       " 14  taxa_uso_credito          100000 non-null  float64\n",
517 |       " 15  idade_historico_credito   100000 non-null  float64\n",
518 |       " 16  investimento_mensal       100000 non-null  float64\n",
519 |       " 17  comportamento_pagamento   100000 non-null  int32  \n",
520 |       " 18  saldo_final_mes           100000 non-null  float64\n",
521 |       " 19  score_credito             100000 non-null  object \n",
522 |       " 20  emprestimo_carro          100000 non-null  int64  \n",
523 |       " 21  emprestimo_casa           100000 non-null  int64  \n",
524 |       " 22  emprestimo_pessoal        100000 non-null  int64  \n",
525 |       " 23  emprestimo_credito        100000 non-null  int64  \n",
526 |       " 24  emprestimo_estudantil     100000 non-null  int64  \n",
527 |       "dtypes: float64(14), int32(3), int64(7), object(1)\n",
528 |       "memory usage: 17.9+ MB\n",
529 |       "None\n"
530 |      ]
531 |     }
532 |    ],
533 |    "source": [
534 |     "from sklearn.preprocessing import LabelEncoder\n",
535 |     "# vai transformar as colunas de texto em números, ex: profissoes vai sair de cientista, professor, mecanico, etc para 0, 1, 2, etc\n",
536 |     "codificador = LabelEncoder()\n",
537 |     "\n",
538 |     "# só não aplicamos na coluna de score_credito que é o nosso objetivo\n",
539 |     "for coluna in tabela.columns:\n",
540 |     "    if tabela[coluna].dtype == \"object\" and coluna != \"score_credito\":\n",
541 |     "        tabela[coluna] = codificador.fit_transform(tabela[coluna])\n",
542 |     "\n",
543 |     "# verificando se realmente todas as colunas foram modificadas\n",
544 |     "print(tabela.info())"
545 |    ]
546 |   },
547 |   {
548 |    "cell_type": "code",
549 |    "execution_count": 13,
550 |    "metadata": {},
551 |    "outputs": [],
552 |    "source": [
553 |     "# escolhendo quais colunas vamos usar para treinar o modelo\n",
554 |     "# y é a coluna que queremos que o modelo calcule\n",
555 |     "# x vai todas as colunas que vamos usar para prever o score de credito, não vamos usar a coluna id_cliente porque ela é um numero qualquer que nao ajuda a previsao\n",
556 |     "x = tabela.drop([\"score_credito\", \"id_cliente\"], axis=1)\n",
557 |     "y = tabela[\"score_credito\"]\n",
558 |     "\n",
559 |     "from sklearn.model_selection import train_test_split\n",
560 |     "\n",
561 |     "# separamos os dados em treino e teste. Treino vamos dar para os modelos aprenderem e teste vamos usar para ver se o modelo aprendeu corretamente\n",
562 |     "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.3, random_state=1)"
563 |    ]
564 |   },
565 |   {
566 |    "cell_type": "code",
567 |    "execution_count": 14,
568 |    "metadata": {},
569 |    "outputs": [
570 |     {
571 |      "data": {
572 |       "text/html": [
573 |        "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
574 |       ],
575 |       "text/plain": [
576 |        "KNeighborsClassifier()"
577 |       ]
578 |      },
579 |      "execution_count": 14,
580 |      "metadata": {},
581 |      "output_type": "execute_result"
582 |     }
583 |    ],
584 |    "source": [
585 |     "from sklearn.ensemble import RandomForestClassifier\n",
586 |     "from sklearn.neighbors import KNeighborsClassifier\n",
587 |     "\n",
588 |     "modelo_arvore = RandomForestClassifier() # modelo arvore de decisao\n",
589 |     "modelo_knn = KNeighborsClassifier() # modelo do KNN (nearest neighbors - vizinhos mais proximos)\n",
590 |     "\n",
591 |     "# treinando os modelos\n",
592 |     "modelo_arvore.fit(x_treino, y_treino)\n",
593 |     "modelo_knn.fit(x_treino, y_treino)"
594 |    ]
595 |   },
596 |   {
597 |    "cell_type": "code",
598 |    "execution_count": 15,
599 |    "metadata": {},
600 |    "outputs": [
601 |     {
602 |      "name": "stdout",
603 |      "output_type": "stream",
604 |      "text": [
605 |       "0.53174\n"
606 |      ]
607 |     }
608 |    ],
609 |    "source": [
610 |     "# se o nosso modelo chutasse tudo \"Standard\", qual seria a acurácia do modelo?\n",
611 |     "contagem_scores = tabela[\"score_credito\"].value_counts()\n",
612 |     "print(contagem_scores['Standard'] / sum(contagem_scores))"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 16,
618 |    "metadata": {},
619 |    "outputs": [
620 |     {
621 |      "name": "stdout",
622 |      "output_type": "stream",
623 |      "text": [
624 |       "0.8248666666666666\n",
625 |       "0.7324\n"
626 |      ]
627 |     }
628 |    ],
629 |    "source": [
630 |     "from sklearn.metrics import accuracy_score\n",
631 |     "\n",
632 |     "# calculamos as previsoes\n",
633 |     "previsao_arvore = modelo_arvore.predict(x_teste)\n",
634 |     "previsao_knn = modelo_knn.predict(x_teste)\n",
635 |     "\n",
636 |     "# comparamos as previsoes com o y_teste\n",
637 |     "# esse score queremos o maior (maior acuracia, mas tb tem que ser maior do que o chute de tudo Standard)\n",
638 |     "print(accuracy_score(y_teste, previsao_arvore))\n",
639 |     "print(accuracy_score(y_teste, previsao_knn))\n",
640 |     "\n"
641 |    ]
642 |   },
643 |   {
644 |    "cell_type": "code",
645 |    "execution_count": 17,
646 |    "metadata": {},
647 |    "outputs": [
648 |     {
649 |      "data": {
650 |       "text/html": [
651 |        "<div>\n",
652 |        "<style scoped>\n",
653 |        "    .dataframe tbody tr th:only-of-type {\n",
654 |        "        vertical-align: middle;\n",
655 |        "    }\n",
656 |        "\n",
657 |        "    .dataframe tbody tr th {\n",
658 |        "        vertical-align: top;\n",
659 |        "    }\n",
660 |        "\n",
661 |        "    .dataframe thead th {\n",
662 |        "        text-align: right;\n",
663 |        "    }\n",
664 |        "</style>\n",
665 |        "<table border=\"1\" class=\"dataframe\">\n",
666 |        "  <thead>\n",
667 |        "    <tr style=\"text-align: right;\">\n",
668 |        "      <th></th>\n",
669 |        "      <th>mes</th>\n",
670 |        "      <th>idade</th>\n",
671 |        "      <th>profissao</th>\n",
672 |        "      <th>salario_anual</th>\n",
673 |        "      <th>num_contas</th>\n",
674 |        "      <th>num_cartoes</th>\n",
675 |        "      <th>juros_emprestimo</th>\n",
676 |        "      <th>num_emprestimos</th>\n",
677 |        "      <th>dias_atraso</th>\n",
678 |        "      <th>num_pagamentos_atrasados</th>\n",
679 |        "      <th>...</th>\n",
680 |        "      <th>taxa_uso_credito</th>\n",
681 |        "      <th>idade_historico_credito</th>\n",
682 |        "      <th>investimento_mensal</th>\n",
683 |        "      <th>comportamento_pagamento</th>\n",
684 |        "      <th>saldo_final_mes</th>\n",
685 |        "      <th>emprestimo_carro</th>\n",
686 |        "      <th>emprestimo_casa</th>\n",
687 |        "      <th>emprestimo_pessoal</th>\n",
688 |        "      <th>emprestimo_credito</th>\n",
689 |        "      <th>emprestimo_estudantil</th>\n",
690 |        "    </tr>\n",
691 |        "  </thead>\n",
692 |        "  <tbody>\n",
693 |        "    <tr>\n",
694 |        "      <th>0</th>\n",
695 |        "      <td>1</td>\n",
696 |        "      <td>31.0</td>\n",
697 |        "      <td>empresario</td>\n",
698 |        "      <td>19300.340</td>\n",
699 |        "      <td>6.0</td>\n",
700 |        "      <td>7.0</td>\n",
701 |        "      <td>17.0</td>\n",
702 |        "      <td>5.0</td>\n",
703 |        "      <td>52.0</td>\n",
704 |        "      <td>19.0</td>\n",
705 |        "      <td>...</td>\n",
706 |        "      <td>29.934186</td>\n",
707 |        "      <td>218.0</td>\n",
708 |        "      <td>44.50951</td>\n",
709 |        "      <td>baixo_gasto_pagamento_baixo</td>\n",
710 |        "      <td>312.487689</td>\n",
711 |        "      <td>1</td>\n",
712 |        "      <td>1</td>\n",
713 |        "      <td>0</td>\n",
714 |        "      <td>0</td>\n",
715 |        "      <td>0</td>\n",
716 |        "    </tr>\n",
717 |        "    <tr>\n",
718 |        "      <th>1</th>\n",
719 |        "      <td>4</td>\n",
720 |        "      <td>32.0</td>\n",
721 |        "      <td>advogado</td>\n",
722 |        "      <td>12600.445</td>\n",
723 |        "      <td>5.0</td>\n",
724 |        "      <td>5.0</td>\n",
725 |        "      <td>10.0</td>\n",
726 |        "      <td>3.0</td>\n",
727 |        "      <td>25.0</td>\n",
728 |        "      <td>18.0</td>\n",
729 |        "      <td>...</td>\n",
730 |        "      <td>28.819407</td>\n",
731 |        "      <td>12.0</td>\n",
732 |        "      <td>0.00000</td>\n",
733 |        "      <td>baixo_gasto_pagamento_medio</td>\n",
734 |        "      <td>300.994163</td>\n",
735 |        "      <td>0</td>\n",
736 |        "      <td>0</td>\n",
737 |        "      <td>0</td>\n",
738 |        "      <td>0</td>\n",
739 |        "      <td>1</td>\n",
740 |        "    </tr>\n",
741 |        "    <tr>\n",
742 |        "      <th>2</th>\n",
743 |        "      <td>2</td>\n",
744 |        "      <td>48.0</td>\n",
745 |        "      <td>empresario</td>\n",
746 |        "      <td>20787.690</td>\n",
747 |        "      <td>8.0</td>\n",
748 |        "      <td>6.0</td>\n",
749 |        "      <td>14.0</td>\n",
750 |        "      <td>7.0</td>\n",
751 |        "      <td>24.0</td>\n",
752 |        "      <td>14.0</td>\n",
753 |        "      <td>...</td>\n",
754 |        "      <td>34.235853</td>\n",
755 |        "      <td>215.0</td>\n",
756 |        "      <td>0.00000</td>\n",
757 |        "      <td>baixo_gasto_pagamento_alto</td>\n",
758 |        "      <td>345.081577</td>\n",
759 |        "      <td>0</td>\n",
760 |        "      <td>1</td>\n",
761 |        "      <td>0</td>\n",
762 |        "      <td>1</td>\n",
763 |        "      <td>0</td>\n",
764 |        "    </tr>\n",
765 |        "  </tbody>\n",
766 |        "</table>\n",
767 |        "<p>3 rows × 23 columns</p>\n",
768 |        "</div>"
769 |       ],
770 |       "text/plain": [
771 |        "   mes  idade   profissao  salario_anual  num_contas  num_cartoes  \\\n",
772 |        "0    1   31.0  empresario      19300.340         6.0          7.0   \n",
773 |        "1    4   32.0    advogado      12600.445         5.0          5.0   \n",
774 |        "2    2   48.0  empresario      20787.690         8.0          6.0   \n",
775 |        "\n",
776 |        "   juros_emprestimo  num_emprestimos  dias_atraso  num_pagamentos_atrasados  \\\n",
777 |        "0              17.0              5.0         52.0                      19.0   \n",
778 |        "1              10.0              3.0         25.0                      18.0   \n",
779 |        "2              14.0              7.0         24.0                      14.0   \n",
780 |        "\n",
781 |        "   ...  taxa_uso_credito idade_historico_credito  investimento_mensal  \\\n",
782 |        "0  ...         29.934186                   218.0             44.50951   \n",
783 |        "1  ...         28.819407                    12.0              0.00000   \n",
784 |        "2  ...         34.235853                   215.0              0.00000   \n",
785 |        "\n",
786 |        "       comportamento_pagamento  saldo_final_mes  emprestimo_carro  \\\n",
787 |        "0  baixo_gasto_pagamento_baixo       312.487689                 1   \n",
788 |        "1  baixo_gasto_pagamento_medio       300.994163                 0   \n",
789 |        "2   baixo_gasto_pagamento_alto       345.081577                 0   \n",
790 |        "\n",
791 |        "  emprestimo_casa  emprestimo_pessoal  emprestimo_credito  \\\n",
792 |        "0               1                   0                   0   \n",
793 |        "1               0                   0                   0   \n",
794 |        "2               1                   0                   1   \n",
795 |        "\n",
796 |        "   emprestimo_estudantil  \n",
797 |        "0                      0  \n",
798 |        "1                      1  \n",
799 |        "2                      0  \n",
800 |        "\n",
801 |        "[3 rows x 23 columns]"
802 |       ]
803 |      },
804 |      "metadata": {},
805 |      "output_type": "display_data"
806 |     },
807 |     {
808 |      "name": "stdout",
809 |      "output_type": "stream",
810 |      "text": [
811 |       "['Poor' 'Good' 'Standard']\n"
812 |      ]
813 |     }
814 |    ],
815 |    "source": [
816 |     "# fazendo novas previsões\n",
817 |     "novos_clientes = pd.read_csv(\"novos_clientes.csv\")\n",
818 |     "display(novos_clientes)\n",
819 |     "for coluna in novos_clientes.columns:\n",
820 |     "    if novos_clientes[coluna].dtype == \"object\" and coluna != \"score_credito\":\n",
821 |     "        novos_clientes[coluna] = codificador.fit_transform(novos_clientes[coluna])\n",
822 |     "\n",
823 |     "previsoes = modelo_arvore.predict(novos_clientes)\n",
824 |     "print(previsoes)"
825 |    ]
826 |   },
827 |   {
828 |    "cell_type": "code",
829 |    "execution_count": 18,
830 |    "metadata": {},
831 |    "outputs": [
832 |     {
833 |      "name": "stdout",
834 |      "output_type": "stream",
835 |      "text": [
836 |       "                                  0\n",
837 |       "mes                        3.983256\n",
838 |       "idade                      4.285039\n",
839 |       "profissao                  3.271311\n",
840 |       "salario_anual              5.025718\n",
841 |       "num_contas                 3.637851\n",
842 |       "num_cartoes                4.441896\n",
843 |       "juros_emprestimo           8.571290\n",
844 |       "num_emprestimos            2.769545\n",
845 |       "dias_atraso                6.743373\n",
846 |       "num_pagamentos_atrasados   4.458836\n",
847 |       "num_verificacoes_credito   5.090539\n",
848 |       "mix_credito                8.224462\n",
849 |       "divida_total              10.409323\n",
850 |       "taxa_uso_credito           5.084289\n",
851 |       "idade_historico_credito    7.750032\n",
852 |       "investimento_mensal        4.857330\n",
853 |       "comportamento_pagamento    2.358480\n",
854 |       "saldo_final_mes            5.510073\n",
855 |       "emprestimo_carro           0.688694\n",
856 |       "emprestimo_casa            0.734250\n",
857 |       "emprestimo_pessoal         0.712559\n",
858 |       "emprestimo_credito         0.699792\n",
859 |       "emprestimo_estudantil      0.692062\n"
860 |      ]
861 |     }
862 |    ],
863 |    "source": [
864 |     "# quais as caracteristicas mais importantes para definir o score de credito?\n",
865 |     "colunas = list(x_teste.columns)\n",
866 |     "importancia = pd.DataFrame(index=colunas, data=modelo_arvore.feature_importances_)\n",
867 |     "importancia = importancia * 100\n",
868 |     "print(importancia)"
869 |    ]
870 |   }
871 |  ],
872 |  "metadata": {
873 |   "kernelspec": {
874 |    "display_name": "Python 3",
875 |    "language": "python",
876 |    "name": "python3"
877 |   },
878 |   "language_info": {
879 |    "codemirror_mode": {
880 |     "name": "ipython",
881 |     "version": 3
882 |    },
883 |    "file_extension": ".py",
884 |    "mimetype": "text/x-python",
885 |    "name": "python",
886 |    "nbconvert_exporter": "python",
887 |    "pygments_lexer": "ipython3",
888 |    "version": "3.10.2"
889 |   },
890 |   "orig_nbformat": 4
891 |  },
892 |  "nbformat": 4,
893 |  "nbformat_minor": 2
894 | }
895 | 


--------------------------------------------------------------------------------
/Python IA/novos_clientes.csv:
--------------------------------------------------------------------------------
1 | mes,idade,profissao,salario_anual,num_contas,num_cartoes,juros_emprestimo,num_emprestimos,dias_atraso,num_pagamentos_atrasados,num_verificacoes_credito,mix_credito,divida_total,taxa_uso_credito,idade_historico_credito,investimento_mensal,comportamento_pagamento,saldo_final_mes,emprestimo_carro,emprestimo_casa,emprestimo_pessoal,emprestimo_credito,emprestimo_estudantil
2 | 1,31.0,empresario,19300.34,6.0,7.0,17.0,5.0,52.0,19.0,7.0,Ruim,2430.21,29.934185732751413,218.0,44.50950984607408,baixo_gasto_pagamento_baixo,312.4876885895601,1,1,0,0,0
3 | 4,32.0,advogado,12600.445,5.0,5.0,10.0,3.0,25.0,18.0,7.0,Normal,569.8,28.819407058304,12.0,0.0,baixo_gasto_pagamento_medio,300.9941628933122,0,0,0,0,1
4 | 2,48.0,empresario,20787.69,8.0,6.0,14.0,7.0,24.0,14.0,3.0,Normal,614.6,34.235852681958825,215.0,0,baixo_gasto_pagamento_alto,345.0815769711927,0,1,0,1,0


--------------------------------------------------------------------------------
/Python PowerUp/codigo.py:
--------------------------------------------------------------------------------
 1 | # Passo a passo do projeto
 2 | # Passo 1: Entrar no sistema da empresa 
 3 |     # https://dlp.hashtagtreinamentos.com/python/intensivao/login
 4 | 
 5 | import pyautogui
 6 | import time
 7 | 
 8 | # pyautogui.write -> escrever um texto
 9 | # pyautogui.press -> apertar 1 tecla
10 | # pyautogui.click -> clicar em algum lugar da tela
11 | # pyautogui.hotkey -> combinação de teclas
12 | pyautogui.PAUSE = 1
13 | 
14 | # abrir o navegador (chrome)
15 | pyautogui.press("win")
16 | pyautogui.write("chrome")
17 | pyautogui.press("enter")
18 | time.sleep(10)
19 | 
20 | # entrar no link 
21 | pyautogui.write("https://dlp.hashtagtreinamentos.com/python/intensivao/login")
22 | pyautogui.press("enter")
23 | time.sleep(10)
24 | 
25 | # Passo 2: Fazer login
26 | # selecionar o campo de email
27 | pyautogui.click(x=422, y=382)
28 | # escrever o seu email
29 | pyautogui.write("paulo@gmail.com")
30 | pyautogui.press("tab") # passando pro próximo campo
31 | pyautogui.write("passwd")
32 | pyautogui.click(x=670, y=544) # clique no botao de login
33 | time.sleep(10)
34 | pyautogui.press("enter")
35 | 
36 | # Passo 3: Importar a base de produtos pra cadastrar
37 | import pandas as pd
38 | 
39 | tabela = pd.read_csv("produtos.csv")
40 | 
41 | # print(tabela)
42 | 
43 | # Passo 4: Cadastrar um produto
44 | for linha in tabela.index:
45 |     # clicar no campo de código
46 |     pyautogui.click(x=410, y=259)
47 |     # pegar da tabela o valor do campo que a gente quer preencher
48 |     codigo = tabela.loc[linha, "codigo"]
49 |     # preencher o campo
50 |     pyautogui.write(str(codigo))
51 |     # passar para o proximo campo
52 |     pyautogui.press("tab")
53 |     # preencher o campo
54 |     pyautogui.write(str(tabela.loc[linha, "marca"]))
55 |     pyautogui.press("tab")
56 |     pyautogui.write(str(tabela.loc[linha, "tipo"]))
57 |     pyautogui.press("tab")
58 |     pyautogui.write(str(tabela.loc[linha, "categoria"]))
59 |     pyautogui.press("tab")
60 |     pyautogui.write(str(tabela.loc[linha, "preco_unitario"]))
61 |     pyautogui.press("tab")
62 |     pyautogui.write(str(tabela.loc[linha, "custo"]))
63 |     pyautogui.press("tab")
64 |     obs = tabela.loc[linha, "obs"]
65 |     if not pd.isna(obs):
66 |         pyautogui.write(str(tabela.loc[linha, "obs"]))
67 |     pyautogui.press("tab")
68 |     pyautogui.press("enter") # cadastra o produto (botao enviar)
69 |     # dar scroll de tudo pra cima
70 |     pyautogui.scroll(5000)
71 |     # Passo 5: Repetir o processo de cadastro até o fim


--------------------------------------------------------------------------------
/Python PowerUp/pegar_posicao.py:
--------------------------------------------------------------------------------
1 | import time
2 | import pyautogui
3 | 
4 | time.sleep(5)
5 | print(pyautogui.position())
6 | 
7 | pyautogui.scroll(200)


--------------------------------------------------------------------------------
/Python PowerUp/produtos.csv:
--------------------------------------------------------------------------------
  1 | codigo,marca,tipo,categoria,preco_unitario,custo,obs
  2 | MOLO000251,Logitech,Mouse,1,25.95,6.50,
  3 | MOLO000192,Logitech,Mouse,2,19.95,5.00,
  4 | CAHA000251,Hashtag,Camisa,1,25.00,11.00,
  5 | CAHA000252,Hashtag,Camisa,2,25.00,11.00,Conferir estoque
  6 | MOMU000111,Multilaser,Mouse,1,11.99,3.40,
  7 | BOHA000251,Hashtag,Bone,1,25.00,11.00,
  8 | BOHA000252,Hashtag,Bone,2,25.00,11.00,
  9 | CAHA000273,Hashtag,Camisa,3,27.50,12.40,Troca de fornecedor
 10 | CAHA000274,Hashtag,Camisa,4,27.50,12.40,
 11 | CAHA000275,Hashtag,Camisa,5,27.50,12.40,
 12 | TELG000821,LG,Televisao,1,820.00,172.20,
 13 | TELG000252,LG,Televisao,2,250.00,62.50,
 14 | TELG000373,LG,Televisao,3,378.00,83.20,
 15 | TELG000524,LG,Televisao,4,520.00,145.60,
 16 | TESA000125,Samsung,Televisao,5,1260.00,378.00,
 17 | TESA000346,Samsung,Televisao,6,340.00,71.40,
 18 | TESA000747,Samsung,Televisao,7,740.00,214.60,
 19 | TESA000108,Samsung,Televisao,8,10000.00,3000.00,
 20 | TESA000729,Samsung,Televisao,9,720.00,201.60,
 21 | TESA0002010,Samsung,Televisao,10,2000.00,440.00,
 22 | TEAO0002511,AOC,Televisao,11,250.00,62.50,
 23 | TEAO0003612,AOC,Televisao,12,360.00,97.20,
 24 | TETO0006413,Toshiba,Televisao,13,640.00,185.60,
 25 | TETO0008014,Toshiba,Televisao,14,800.00,192.00,
 26 | TETO0006015,Toshiba,Televisao,15,600.00,144.00,
 27 | TESE0005016,SEMP,Televisao,16,500.00,125.00,
 28 | TESE0003817,SEMP,Televisao,17,380.00,91.20,
 29 | TESE0005018,SEMP,Televisao,18,500.00,140.00,
 30 | TEPH0004019,Philco,Televisao,19,400.00,120.00,
 31 | TEPH0002720,Philco,Televisao,20,279.80,56.00,
 32 | TETC0001521,TCL,Televisao,21,158.00,36.30,
 33 | TETC0002222,TCL,Televisao,22,220.00,59.40,
 34 | REGA000441,Garmin,Relogio,1,440.00,96.80,
 35 | REGA000722,Garmin,Relogio,2,720.00,187.20,
 36 | REXI000463,Xiaomi,Relogio,3,46.40,11.60,
 37 | REXI000744,Xiaomi,Relogio,4,74.00,15.50,
 38 | REXI000365,Xiaomi,Relogio,5,36.00,7.90,
 39 | REXI000116,Xiaomi,Relogio,6,114.00,23.90,
 40 | RESA000287,Samsung,Relogio,7,280.00,72.80,
 41 | RESA000298,Samsung,Relogio,8,290.00,72.50,
 42 | RESA000389,Samsung,Relogio,9,380.00,110.20,
 43 | RESA0003210,Samsung,Relogio,10,320.00,83.20,
 44 | CEAP000101,Apple,Celular,1,1099.00,329.70,
 45 | CEAP000112,Apple,Celular,2,1199.00,287.80,
 46 | CEAP000133,Apple,Celular,3,1399.00,377.70,
 47 | CEAP000154,Apple,Celular,4,1599.00,367.80,
 48 | CEAP000995,Apple,Celular,5,999.00,269.70,
 49 | CEAP000106,Apple,Celular,6,1099.00,329.70,
 50 | CEAP000127,Apple,Celular,7,1299.00,298.80,
 51 | CEAP000148,Apple,Celular,8,1499.00,329.80,
 52 | CEAP000799,Apple,Celular,9,799.00,215.70,
 53 | CEAP0008910,Apple,Celular,10,899.00,197.80,
 54 | CEAP0001011,Apple,Celular,11,1099.00,329.70,
 55 | CEAP0006912,Apple,Celular,12,699.00,167.80,
 56 | CEAP0007913,Apple,Celular,13,799.00,215.70,
 57 | CEAP0009914,Apple,Celular,14,999.00,269.70,
 58 | CEAP0004215,Apple,Celular,15,429.00,115.80,
 59 | CEAP0004716,Apple,Celular,16,479.00,138.90,
 60 | CEAP0005717,Apple,Celular,17,579.00,115.80,
 61 | CEAP0009418,Apple,Celular,18,949.00,256.20,
 62 | CEAP0001019,Apple,Celular,19,1049.00,304.20,
 63 | CEAP0001220,Apple,Celular,20,1249.00,362.20,
 64 | CEAP0001421,Apple,Celular,21,1449.00,347.80,
 65 | CEAP0008422,Apple,Celular,22,849.00,178.30,
 66 | CEAP0009423,Apple,Celular,23,949.00,256.20,
 67 | CEAP0001124,Apple,Celular,24,1149.00,252.80,
 68 | CEAP0001325,Apple,Celular,25,1349.00,296.80,
 69 | CEAP0006426,Apple,Celular,26,649.00,181.70,
 70 | CEAP0007427,Apple,Celular,27,749.00,157.30,
 71 | CEAP0009428,Apple,Celular,28,949.00,256.20,
 72 | CEAP0005429,Apple,Celular,29,549.00,115.30,
 73 | CEAP0006430,Apple,Celular,30,649.00,181.70,
 74 | CEAP0008431,Apple,Celular,31,849.00,178.30,
 75 | CEAP0005932,Apple,Celular,32,599.00,137.80,
 76 | CEAP0006933,Apple,Celular,33,699.00,181.70,
 77 | CEAP0005434,Apple,Celular,34,549.00,120.80,
 78 | CEAP0006435,Apple,Celular,35,649.00,142.80,
 79 | CEAP0004436,Apple,Celular,36,449.00,103.30,
 80 | CEAP0004937,Apple,Celular,37,499.00,129.70,
 81 | CEAP0004938,Apple,Celular,38,499.00,124.80,
 82 | CEAP0005439,Apple,Celular,39,549.00,142.70,
 83 | CESA0002040,Samsung,Celular,40,200.00,48.00,
 84 | NODE000441,Dell,Notebook,1,448.00,134.40,
 85 | NODE000512,Dell,Notebook,2,512.00,107.50,
 86 | NOLE000183,Lenovo,Notebook,3,188.61,41.50,
 87 | NOAC000224,Acer,Notebook,4,227.00,65.80,
 88 | NOHP000215,HP,Notebook,5,214.00,49.20,
 89 | NOMI000426,Microsoft,Notebook,6,429.00,90.10,
 90 | NOHP000797,HP,Notebook,7,799.00,199.80,
 91 | NOHP000238,HP,Notebook,8,239.88,64.80,
 92 | NOLE000989,Lenovo,Notebook,9,989.99,217.80,
 93 | NOHP0005210,HP,Notebook,10,529.00,111.10,
 94 | NOSA0001311,Samsung,Notebook,11,1349.99,270.00,
 95 | NOHP0003512,HP,Notebook,12,359.00,100.50,
 96 | NOAS0006213,Asus,Notebook,13,629.99,189.00,
 97 | RESA000501,Samsung,Relogio,1,50.00,14.50,
 98 | RESA000602,Samsung,Relogio,2,60.00,15.60,
 99 | REAP000783,Apple,Relogio,3,789.00,228.80,
100 | CESA000301,Samsung,Celular,1,300.00,66.00,
101 | CESA000402,Samsung,Celular,2,400.00,96.00,
102 | CESA000103,Samsung,Celular,3,100.00,28.00,
103 | CESA000154,Samsung,Celular,4,150.00,31.50,
104 | CESA000205,Samsung,Celular,5,200.00,56.00,
105 | CESA000276,Samsung,Celular,6,270.00,81.00,
106 | CESA000107,Samsung,Celular,7,100.00,27.00,
107 | CESA000808,Samsung,Celular,8,800.00,176.00,
108 | CESA000909,Samsung,Celular,9,900.00,198.00,
109 | CESA0001210,Samsung,Celular,10,120.00,31.20,
110 | CESA0001511,Samsung,Celular,11,150.00,26.97,
111 | CESA0002012,Samsung,Celular,12,200.00,92.81,
112 | CEXI0002513,Xiaomi,Celular,13,257.00,74.50,
113 | CEXI0003114,Xiaomi,Celular,14,317.00,133.30,
114 | CEXI0001515,Xiaomi,Celular,15,159.00,42.90,
115 | CEXI0002116,Xiaomi,Celular,16,219.00,56.90,
116 | CEXI0002417,Xiaomi,Celular,17,249.00,96.80,
117 | CEXI0003218,Xiaomi,Celular,18,329.00,75.70,
118 | CEXI0002919,Xiaomi,Celular,19,299.00,38.96,
119 | CEXI0002420,Xiaomi,Celular,20,249.00,104.80,
120 | CEXI0002521,Xiaomi,Celular,21,250.00,145.28,
121 | CEXI0002722,Xiaomi,Celular,22,279.00,49.98,
122 | CEXI0003523,Xiaomi,Celular,23,350.00,122.27,
123 | CEMO0006924,Motorola,Celular,24,69.00,18.60,
124 | CEMO0009925,Motorola,Celular,25,99.00,25.70,
125 | NOLE000741,Lenovo,Notebook,1,749.00,224.70,
126 | NODE000492,Dell,Notebook,2,498.00,109.60,
127 | NOAS000153,Asus,Notebook,3,1599.00,415.70,
128 | NOHP000594,HP,Notebook,4,599.00,131.80,
129 | NOAC000525,Acer,Notebook,5,529.99,143.10,
130 | NOAC000216,Acer,Notebook,6,2199.00,483.80,
131 | NOAC000127,Acer,Notebook,7,1299.00,285.80,
132 | NODE000608,Dell,Notebook,8,600.00,132.00,
133 | NODE000749,Dell,Notebook,9,740.00,170.20,
134 | NOHP0007510,HP,Notebook,10,750.00,210.00,
135 | NOLE0009911,Lenovo,Notebook,11,999.00,259.70,
136 | CAHA000271,Hashtag,Camisa,1,27.50,12.40,
137 | TELO000131,Logitech,Teclado,1,138.00,37.30,
138 | TEDE000362,Dell,Teclado,2,36.00,8.30,
139 | TELO000443,Logitech,Teclado,3,44.00,11.00,
140 | TEDE000484,Dell,Teclado,4,48.00,11.50,
141 | MOLG000121,LG,Monitor,1,129.00,34.80,
142 | MOSA000152,Samsung,Monitor,2,159.99,41.60,
143 | MOLG000173,LG,Monitor,3,1796.99,467.20,
144 | MOHA000251,Hashtag,Mochila,1,25.00,11.00,
145 | MOHA000252,Hashtag,Mochila,2,25.00,11.00,
146 | CAHA000253,Hashtag,Camisa,3,25.00,11.00,
147 | CAHA000254,Hashtag,Camisa,4,25.00,11.00,
148 | CHHA000275,Hashtag,Chaveiro,5,27.00,12.40,
149 | CAHA000276,Hashtag,Camisa,6,27.00,12.40,
150 | CAHA000277,Hashtag,Camisa,7,27.00,12.40,
151 | CAHA000278,Hashtag,Camisa,8,27.00,12.40,
152 | CAHA0009.9,Hashtag,Camisa,9,9.90,4.50,
153 | CAHA0004510,Hashtag,Casaco,10,45.90,19.70,
154 | CAHA0004511,Hashtag,Casaco,11,45.90,19.70,
155 | CAHA0004512,Hashtag,Casaco,12,45.90,19.70,
156 | CAHA0004513,Hashtag,Casaco,13,45.90,19.70,
157 | CAHA0004814,Hashtag,Casaco,14,48.90,20.00,
158 | CAHA0004815,Hashtag,Casaco,15,48.90,20.00,
159 | CAHA0004816,Hashtag,Casaco,16,48.90,20.00,
160 | CAHA0004817,Hashtag,Casaco,17,48.90,20.00,
161 | CAHA0004518,Hashtag,Casaco,18,45.90,19.70,
162 | CAHA0004519,Hashtag,Casaco,19,45.90,19.70,
163 | CAHA0004520,Hashtag,Casaco,20,45.90,19.70,
164 | CAHA0004521,Hashtag,Casaco,21,45.90,19.70,
165 | MOMU0004.1,Multilaser,Mouse,1,4.99,1.20,
166 | MOMU0003.2,Multilaser,Mouse,2,3.99,1.30,
167 | MOLO0006.3,Logitech,Mouse,3,6.99,2.30,
168 | TELG000111,LG,Teclado,1,110.00,29.70,
169 | CAHA000481,Hashtag,Casaco,1,48.90,20.00,
170 | CAHA000482,Hashtag,Casaco,2,48.90,20.00,
171 | MOSP000221,Spectre,Monitor,1,229.99,59.80,
172 | MOSP000132,Spectre,Monitor,2,132.72,38.50,
173 | MOSP0007.3,Spectre,Mouse,3,7.99,2.00,
174 | TELG000201,LG,Teclado,1,20.00,4.60,
175 | MOLG000782,LG,Monitor,2,789.99,221.20,
176 | BEHA000481,Hashtag,Bermuda,1,48.90,20.00,
177 | BEHA000482,Hashtag,Bermuda,2,48.90,20.00,
178 | CAHA000123,Hashtag,Casaco,3,12.90,6.10,
179 | CAHA000124,Hashtag,Casaco,4,12.90,6.10,
180 | NOAC000791,Acer,Notebook,1,799.00,159.80,
181 | NOMU000192,Multilaser,Notebook,2,199.00,41.80,
182 | NOHP000943,HP,Notebook,3,949.00,237.30,
183 | NOAP000144,Apple,Notebook,4,1499.00,419.70,
184 | NOAP000255,Apple,Notebook,5,2549.00,688.20,
185 | NOAP000646,Apple,Notebook,6,6400.00,1280.00,
186 | NOAP000247,Apple,Notebook,7,2499.00,599.80,
187 | NOAP000128,Apple,Notebook,8,1299.00,337.70,
188 | NOAP000599,Apple,Notebook,9,5999.00,1679.70,
189 | TETC000121,TCL,Televisao,1,1200.00,240.00,
190 | TEVI000152,Vizio,Televisao,2,159.00,36.60,
191 | TEVI000443,Vizio,Televisao,3,440.00,110.00,
192 | TEVI000244,Vizio,Televisao,4,240.00,69.60,
193 | TEIN000255,Insignia,Televisao,5,250.00,65.00,
194 | TEIN000346,Insignia,Televisao,6,343.00,82.30,
195 | TEIN000187,Insignia,Televisao,7,189.99,45.60,
196 | TELG000398,LG,Televisao,8,390.00,93.60,
197 | TELG000559,LG,Televisao,9,550.00,148.50,
198 | TESO0001210,Sony,Televisao,10,1200.00,240.00,
199 | TESO0003311,Sony,Televisao,11,3300.00,726.00,
200 | TESO0006012,Sony,Televisao,12,600.00,138.00,
201 | TESO0005913,Sony,Televisao,13,599.00,131.80,
202 | REAP000101,Apple,Relogio,1,1080.00,280.80,
203 | REAP000702,Apple,Relogio,2,700.00,182.00,
204 | REAP000643,Apple,Relogio,3,640.00,179.20,
205 | REAP000504,Apple,Relogio,4,500.00,110.00,
206 | REAP000485,Apple,Relogio,5,480.00,120.00,
207 | REAP000446,Apple,Relogio,6,440.00,118.80,
208 | REAP000327,Apple,Relogio,7,320.00,92.80,
209 | REGA000928,Garmin,Relogio,8,920.00,239.20,
210 | REGA000809,Garmin,Relogio,9,800.00,160.00,
211 | RESA0002410,Samsung,Relogio,10,240.00,55.20,
212 | CaCA000581,Canon,Camera,1,589.00,176.70,
213 | CaCA000502,Canon,Camera,2,509.00,112.00,
214 | CaCA000483,Canon,Camera,3,489.00,107.60,
215 | CaCA000894,Canon,Camera,4,899.00,260.70,
216 | MOLO000151,Logitech,Mouse,1,15.99,5.10,
217 | TELG000101,LG,Teclado,1,10.00,2.50,
218 | MOSA000191,Samsung,Monitor,1,199.99,52.00,
219 | CaNI000131,Nikon,Camera,1,1396.00,349.00,
220 | CaNI000272,Nikon,Camera,2,2796.95,643.30,
221 | CaNI000793,Nikon,Camera,3,796.95,239.10,
222 | CaNI000214,Nikon,Camera,4,2196.95,527.30,
223 | MODE000191,Dell,Mouse,1,19.99,5.80,
224 | TEMU000211,Multilaser,Teclado,1,21.99,4.40,
225 | MOSA000121,Samsung,Monitor,1,129.99,45.50,
226 | MODE000141,Dell,Mouse,1,14.99,4.60,
227 | TEMU0008.1,Multilaser,Teclado,1,8.99,1.80,
228 | MOSA000171,Samsung,Monitor,1,179.99,46.80,
229 | MODE000281,Dell,Mouse,1,28.99,8.70,
230 | CaNI000251,Nikon,Camera,1,2596.95,811.10,
231 | CaSO000212,Sony,Camera,2,2198.00,461.60,
232 | CaSO000153,Sony,Camera,3,1598.00,479.40,
233 | CaSO000124,Sony,Camera,4,1298.00,298.50,
234 | CaSO000165,Sony,Camera,5,1698.00,441.50,
235 | CaSO000996,Sony,Camera,6,998.00,299.40,
236 | CaSO000347,Sony,Camera,7,3498.00,874.50,
237 | ACAP000191,Apple,Acessorios,1,19.00,3.80,
238 | ACAP000222,Apple,Acessorios,2,22.00,4.40,
239 | ACAP000293,Apple,Acessorios,3,29.00,5.80,
240 | ACAP000194,Apple,Acessorios,4,19.00,3.80,
241 | ACAP000295,Apple,Acessorios,5,29.00,5.80,
242 | ACSA000196,Samsung,Acessorios,6,19.99,6.50,
243 | ACSA000247,Samsung,Acessorios,7,24.99,8.20,
244 | ACSA000298,Samsung,Acessorios,8,29.99,8.90,
245 | ACSA0008.9,Samsung,Acessorios,9,8.90,2.40,
246 | ACSA0001410,Samsung,Acessorios,10,14.90,3.20,
247 | ACSA0001211,Samsung,Acessorios,11,12.50,2.80,
248 | NOAP000501,Apple,Notebook,1,5099.00,1070.80,
249 | NODE000502,Dell,Notebook,2,5099.00,1478.70,
250 | NOAC000123,Acer,Notebook,3,1249.00,374.70,
251 | NODE000544,Dell,Notebook,4,549.00,137.30,
252 | NOSA000395,Samsung,Notebook,5,399.00,103.70,
253 | NOLE000346,Lenovo,Notebook,6,349.00,101.20,
254 | NOSA000547,Samsung,Notebook,7,549.00,115.30,
255 | NOLE000298,Lenovo,Notebook,8,299.00,65.80,
256 | NOMI000999,Microsoft,Notebook,9,999.00,239.80,
257 | NOMI0002110,Microsoft,Notebook,10,2199.00,637.70,
258 | NOMI0001911,Microsoft,Notebook,11,1999.00,419.80,
259 | NOMI0003612,Microsoft,Notebook,12,3600.00,972.00,
260 | NOLE0009913,Lenovo,Notebook,13,999.00,269.70,
261 | NOAC0007014,Acer,Notebook,14,700.00,154.00,
262 | NOCO0003815,Compaq,Notebook,15,380.00,114.00,
263 | NOPO0003416,Positivo,Notebook,16,349.00,104.70,
264 | NOPO0003217,Positivo,Notebook,17,320.00,80.00,
265 | NOVA0003118,Vaio,Notebook,18,319.00,86.10,
266 | NOHP0004919,HP,Notebook,19,4999.00,999.80,
267 | NOAS0001920,Asus,Notebook,20,1999.00,539.70,
268 | CEMO000101,Motorola,Celular,1,100.00,24.00,
269 | CEMO000152,Motorola,Celular,2,150.00,45.00,
270 | CEMO000893,Motorola,Celular,3,89.00,26.70,
271 | CEMO000114,Motorola,Celular,4,119.00,29.80,
272 | CEMO000695,Motorola,Celular,5,69.00,17.30,
273 | CEMO000996,Motorola,Celular,6,99.00,28.70,
274 | CEMO000197,Motorola,Celular,7,199.00,39.80,
275 | NOAS000691,Asus,Notebook,1,6999.00,1399.80,
276 | NOAS000702,Asus,Notebook,2,700.00,161.00,
277 | NOLG000133,LG,Notebook,3,1393.00,334.30,
278 | NOHP000824,HP,Notebook,4,820.00,164.00,
279 | NOHP000165,HP,Notebook,5,1600.00,368.00,
280 | NODE000126,Dell,Notebook,6,1220.00,244.00,
281 | NOLE000147,Lenovo,Notebook,7,1487.00,386.60,
282 | NOLE000248,Lenovo,Notebook,8,249.00,49.80,
283 | NORA000399,Razer,Notebook,9,3999.00,1079.70,
284 | NORA0001110,Razer,Notebook,10,1199.00,299.80,
285 | NORA0001911,Razer,Notebook,11,1999.00,439.80,
286 | NORA0007912,Razer,Notebook,12,7999.00,2159.70,
287 | NOLE0001713,Lenovo,Notebook,13,1750.00,455.00,
288 | NODE0001614,Dell,Notebook,14,1600.00,352.00,
289 | ACAP000391,Apple,Acessorios,1,39.99,11.90,
290 | ACAP000192,Apple,Acessorios,2,19.00,3.80,
291 | ACSA0009.3,Samsung,Acessorios,3,9.55,2.10,
292 | CEMO000271,Motorola,Celular,1,279.00,72.50,
293 | FOMO000152,Motorola,Fone,2,150.00,33.00,
294 | CEMO000223,Motorola,Celular,3,229.00,55.00,


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/README.md:
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1 | # Curso de Python
2 | Repositório criado para guardar os códigos desenvolvidos no curso "Jornada Python", da Hashtag Treinamentos
3 | 
4 | ## Certificado de Conclusão
5 | 
6 | ![Alt text](./Certificate/certificado-jornada-python.png "Certificado de Conclusão do Curso")
7 | 


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