diff --git a/Datasets b/Datasets
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Datasets
@@ -0,0 +1 @@
+
diff --git a/README.md b/README.md
index 1a30b99..065c953 100644
--- a/README.md
+++ b/README.md
@@ -1,47 +1,47 @@
-# 📊 Previsão de Estoque Inteligente na AWS com [SageMaker Canvas](https://aws.amazon.com/pt/sagemaker/canvas/)
-
-Bem-vindo ao desafio de projeto "Previsão de Estoque Inteligente na AWS com SageMaker Canvas. Neste Lab DIO, você aprenderá a usar o SageMaker Canvas para criar previsões de estoque baseadas em Machine Learning (ML). Siga os passos abaixo para completar o desafio!
-
-## 📋 Pré-requisitos
-
-Antes de começar, certifique-se de ter uma conta na AWS. Se precisar de ajuda para criar sua conta, confira nosso repositório [AWS Cloud Quickstart](https://github.com/digitalinnovationone/aws-cloud-quickstart).
+# 📊 Estoque Inteligente na AWS com [SageMaker Canvas](https://aws.amazon.com/pt/sagemaker/canvas/)
 
+Neste projeto utilizei do ChatGPT e SageMaker Canvas para desenvolver um sistema de estoque inteligente com Machine learning (ML).
 
 ## 🎯 Objetivos Deste Desafio de Projeto (Lab)
 
-![image](https://github.com/digitalinnovationone/lab-aws-sagemaker-canvas-estoque/assets/730492/72f5c21f-5562-491e-aa42-2885a3184650)
+O objetivo deste desafio é aprimorar minhas habilidade com Machine Learning low-code, aprender mais sobre o Amazon SageMaker e principalemente aprender como a inteligência artificial pode ajudar no mercado profissional ajudando empresas com tomada de decisões e previsões futuras.
 
-- Dê um fork neste projeto e reescreva este `README.md`. Sinta-se à vontade para detalhar todo o processo de criação do seu Modelo de ML para uma "Previsão de Estoque Inteligente".
-- Para isso, siga o [passo a passo] descrito a seguir e evolua as suas habilidades em ML no-code com o Amazon SageMaker Canvas.
-- Ao concluir, envie a URL do seu repositório com a solução na plataforma da DIO.
+## 🚀 Passo a Passo
 
+### 1. Desenvolver Dataset
 
-## 🚀 Passo a Passo
+-   Utilizei o ChatGPT para desenvolver um DataSet com informações fícticias utilizando o seguinte prompt:
+
+Crie um arquivo CVS contendo as colunas (ID_Produto, ID_Cliente, Valor, Data, Quantidade_estoque).
+utilizarei estes dados para treinamento de um modelo de estoque inteligente.
 
-### 1. Selecionar Dataset
+{REGRAS}
 
--   Navegue até a pasta `datasets` deste repositório. Esta pasta contém os datasets que você poderá escolher para treinar e testar seu modelo de ML. Sinta-se à vontade para gerar/enriquecer seus próprios datasets, quanto mais você se engajar, mais relevante esse projeto será em seu portfólio.
--   Escolha o dataset que você usará para treinar seu modelo de previsão de estoque.
--   Faça o upload do dataset no SageMaker Canvas.
+- Sempre que um produto chegar a quantidade 10 reabasteça o material,
+- Preencha o arquivo com no minimo 6 compras de produtos variados ficticios por dia.
+- Preencha ao arquivo comecando do dia 01-07-2024 terminando em 17-07-2-2024
 
 ### 2. Construir/Treinar
 
--   No SageMaker Canvas, importe o dataset que você selecionou.
--   Configure as variáveis de entrada e saída de acordo com os dados.
--   Inicie o treinamento do modelo. Isso pode levar algum tempo, dependendo do tamanho do dataset.
+-   No SageMaker Canvas, importei o dataset criado.
+-   Configurei as variáveis de entrada e saída.
+-   Iniciei o treinamento do modelo.
 
 ### 3. Analisar
 
--   Após o treinamento, examine as métricas de performance do modelo.
--   Verifique as principais características que influenciam as previsões.
--   Faça ajustes no modelo se necessário e re-treine até obter um desempenho satisfatório.
+-   Após o treinamento, examinei as métricas de performance do modelo.
+-   Verifiquei as principais características que influenciam as previsões que foram a quantidade de material em estoque (quantidade_estoque) e o valor do produto (valor).
 
 ### 4. Prever
 
--   Use o modelo treinado para fazer previsões de estoque.
--   Exporte os resultados e analise as previsões geradas.
--   Documente suas conclusões e qualquer insight obtido a partir das previsões.
+-   Usei o modelo treinado para fazer previsões de estoque.
+-   Analisando as previsões geradas notamos claramente que o principal influenciador nos nossos resultados é a quantidade de material em estoque, ja que quando aumentamos a quantidade em estoque de 50 para 10 notamos uma diminuição significativa de vendas para todos os produtos.
+-   Notamos também que o produto P007 não tem alterações relevantes nas vendas independente da quantidade em estoque ou valor, o que pode indicar um grande volume de saída, possibilitando talvez o aumento do estoque deste material.
+-   O produto P005 teve um aumento de 63% de saída quanto alteramos o valor de 70 para 50.
+-   O produto P003 teve um aumento de 80% de saída quando aumentamos a quantidade em estoque de 10 para 100, o que pode indicar um maior volume de compras em maiores quantidades.
+-   Os demais produtos possuem alterações relevantes somente quando diminuimos o período analisado, consequentimente alterações negativas, o que pode indicar a necessidade de mais atenção nesses produtos.
 
-## 🤔 Dúvidas?
+## Agradecimentos
 
-Esperamos que esta experiência tenha sido enriquecedora e que você tenha aprendido mais sobre Machine Learning aplicado a problemas reais. Se tiver alguma dúvida, não hesite em abrir uma issue neste repositório ou entrar em contato com a equipe da DIO.
+Espero que tenham gostado do meu projeto, se quiserem podem me seguir aqui no Github e no linkedin para mais projetos:
+https://www.linkedin.com/in/andr%C3%A9-abichabicki-46b5b1193/
diff --git a/datasets/dataset-1000-com-preco-promocional-e-renovacao-estoque.csv b/datasets/dataset-1000-com-preco-promocional-e-renovacao-estoque.csv
deleted file mode 100644
index b53468b..0000000
--- a/datasets/dataset-1000-com-preco-promocional-e-renovacao-estoque.csv
+++ /dev/null
@@ -1,1001 +0,0 @@
-ID_PRODUTO,DATA_EVENTO,PRECO,FLAG_PROMOCAO,QUANTIDADE_ESTOQUE
-1000,2023-12-31,138.43,1,100
-1001,2023-12-31,75.08,0,100
-1002,2023-12-31,58.84,0,100
-1003,2023-12-31,61.96,0,100
-1004,2023-12-31,20.34,0,100
-1005,2023-12-31,29.65,1,100
-1006,2023-12-31,187.04,0,100
-1007,2023-12-31,23.09,0,100
-1008,2023-12-31,173.21,0,100
-1009,2023-12-31,66.31,0,100
-1010,2023-12-31,182.28,0,100
-1011,2023-12-31,48.96,0,100
-1012,2023-12-31,21.94,0,100
-1013,2023-12-31,72.67,1,100
-1014,2023-12-31,141.87,0,100
-1015,2023-12-31,27.82,0,100
-1016,2023-12-31,30.19,0,100
-1017,2023-12-31,22.68,0,100
-1018,2023-12-31,44.2,0,100
-1019,2023-12-31,100.19,0,100
-1020,2023-12-31,129.13,0,100
-1021,2023-12-31,21.24,1,100
-1022,2023-12-31,85.71,0,100
-1023,2023-12-31,143.12,0,100
-1024,2023-12-31,65.27,1,100
-1000,2024-01-01,153.81,0,95
-1001,2024-01-01,67.57,1,88
-1002,2024-01-01,52.96,1,94
-1003,2024-01-01,61.96,0,95
-1004,2024-01-01,20.34,0,94
-1005,2024-01-01,32.94,0,83
-1006,2024-01-01,187.04,0,100
-1007,2024-01-01,23.09,0,100
-1008,2024-01-01,173.21,0,86
-1009,2024-01-01,59.68,1,84
-1010,2024-01-01,182.28,0,82
-1011,2024-01-01,48.96,0,83
-1012,2024-01-01,21.94,0,82
-1013,2024-01-01,72.67,1,84
-1014,2024-01-01,141.87,0,85
-1015,2024-01-01,25.04,1,99
-1016,2024-01-01,30.19,0,97
-1017,2024-01-01,22.68,0,93
-1018,2024-01-01,44.2,0,95
-1019,2024-01-01,90.17,1,98
-1020,2024-01-01,129.13,0,91
-1021,2024-01-01,21.24,1,86
-1022,2024-01-01,85.71,0,83
-1023,2024-01-01,143.12,0,89
-1024,2024-01-01,72.52,0,99
-1000,2024-01-02,138.43,1,89
-1001,2024-01-02,75.08,0,81
-1002,2024-01-02,58.84,0,80
-1003,2024-01-02,61.96,0,85
-1004,2024-01-02,20.34,0,83
-1005,2024-01-02,32.94,0,67
-1006,2024-01-02,168.34,1,85
-1007,2024-01-02,23.09,0,81
-1008,2024-01-02,173.21,0,76
-1009,2024-01-02,66.31,0,77
-1010,2024-01-02,164.05,1,81
-1011,2024-01-02,48.96,0,68
-1012,2024-01-02,21.94,0,63
-1013,2024-01-02,72.67,1,66
-1014,2024-01-02,141.87,0,85
-1015,2024-01-02,27.82,0,84
-1016,2024-01-02,30.19,0,95
-1017,2024-01-02,22.68,0,76
-1018,2024-01-02,39.78,1,76
-1019,2024-01-02,100.19,0,88
-1020,2024-01-02,129.13,0,90
-1021,2024-01-02,21.24,1,84
-1022,2024-01-02,77.14,1,73
-1023,2024-01-02,128.81,1,80
-1024,2024-01-02,72.52,0,88
-1000,2024-01-03,153.81,0,80
-1001,2024-01-03,75.08,0,72
-1002,2024-01-03,58.84,0,75
-1003,2024-01-03,61.96,0,78
-1004,2024-01-03,18.31,1,67
-1005,2024-01-03,32.94,0,67
-1006,2024-01-03,168.34,1,70
-1007,2024-01-03,23.09,0,77
-1008,2024-01-03,155.89,1,67
-1009,2024-01-03,66.31,0,72
-1010,2024-01-03,164.05,1,67
-1011,2024-01-03,48.96,0,50
-1012,2024-01-03,21.94,0,58
-1013,2024-01-03,80.74,0,49
-1014,2024-01-03,141.87,0,72
-1015,2024-01-03,27.82,0,82
-1016,2024-01-03,30.19,0,94
-1017,2024-01-03,22.68,0,65
-1018,2024-01-03,44.2,0,58
-1019,2024-01-03,100.19,0,74
-1020,2024-01-03,129.13,0,86
-1021,2024-01-03,23.6,0,66
-1022,2024-01-03,85.71,0,55
-1023,2024-01-03,128.81,1,61
-1024,2024-01-03,72.52,0,76
-1000,2024-01-04,153.81,0,76
-1001,2024-01-04,75.08,0,54
-1002,2024-01-04,58.84,0,74
-1003,2024-01-04,61.96,0,64
-1004,2024-01-04,20.34,0,51
-1005,2024-01-04,32.94,0,49
-1006,2024-01-04,187.04,0,54
-1007,2024-01-04,23.09,0,69
-1008,2024-01-04,173.21,0,57
-1009,2024-01-04,66.31,0,67
-1010,2024-01-04,164.05,1,50
-1011,2024-01-04,48.96,0,39
-1012,2024-01-04,19.75,1,58
-1013,2024-01-04,80.74,0,37
-1014,2024-01-04,127.68,1,60
-1015,2024-01-04,27.82,0,67
-1016,2024-01-04,30.19,0,76
-1017,2024-01-04,22.68,0,65
-1018,2024-01-04,44.2,0,51
-1019,2024-01-04,90.17,1,65
-1020,2024-01-04,129.13,0,86
-1021,2024-01-04,23.6,0,60
-1022,2024-01-04,85.71,0,44
-1023,2024-01-04,128.81,1,48
-1024,2024-01-04,72.52,0,57
-1000,2024-01-05,153.81,0,57
-1001,2024-01-05,75.08,0,36
-1002,2024-01-05,58.84,0,55
-1003,2024-01-05,55.76,1,59
-1004,2024-01-05,18.31,1,47
-1005,2024-01-05,29.65,1,35
-1006,2024-01-05,187.04,0,49
-1007,2024-01-05,23.09,0,63
-1008,2024-01-05,173.21,0,56
-1009,2024-01-05,66.31,0,48
-1010,2024-01-05,164.05,1,35
-1011,2024-01-05,48.96,0,25
-1012,2024-01-05,21.94,0,56
-1013,2024-01-05,80.74,0,26
-1014,2024-01-05,141.87,0,56
-1015,2024-01-05,27.82,0,58
-1016,2024-01-05,30.19,0,70
-1017,2024-01-05,20.41,1,49
-1018,2024-01-05,44.2,0,50
-1019,2024-01-05,100.19,0,58
-1020,2024-01-05,116.22,1,67
-1021,2024-01-05,23.6,0,54
-1022,2024-01-05,85.71,0,36
-1023,2024-01-05,143.12,0,40
-1024,2024-01-05,72.52,0,56
-1000,2024-01-06,153.81,0,39
-1001,2024-01-06,67.57,1,27
-1002,2024-01-06,52.96,1,43
-1003,2024-01-06,61.96,0,46
-1004,2024-01-06,20.34,0,38
-1005,2024-01-06,32.94,0,27
-1006,2024-01-06,187.04,0,49
-1007,2024-01-06,23.09,0,52
-1008,2024-01-06,155.89,1,41
-1009,2024-01-06,66.31,0,45
-1010,2024-01-06,182.28,0,28
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-1016,2024-01-06,30.19,0,62
-1017,2024-01-06,22.68,0,35
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-1019,2024-01-06,100.19,0,54
-1020,2024-01-06,129.13,0,53
-1021,2024-01-06,23.6,0,46
-1022,2024-01-06,85.71,0,19
-1023,2024-01-06,143.12,0,30
-1024,2024-01-06,72.52,0,42
-1000,2024-01-07,153.81,0,25
-1001,2024-01-07,75.08,0,12
-1002,2024-01-07,58.84,0,33
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-1004,2024-01-07,18.31,1,37
-1005,2024-01-07,29.65,1,15
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-1009,2024-01-07,66.31,0,29
-1010,2024-01-07,164.05,1,10
-1011,2024-01-07,48.96,0,4
-1012,2024-01-07,21.94,0,38
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-1016,2024-01-07,27.17,1,58
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-1018,2024-01-07,44.2,0,39
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-1020,2024-01-07,129.13,0,40
-1021,2024-01-07,23.6,0,38
-1022,2024-01-07,85.71,0,7
-1023,2024-01-07,143.12,0,13
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-1002,2024-01-08,58.84,0,19
-1003,2024-01-08,61.96,0,21
-1004,2024-01-08,20.34,0,18
-1005,2024-01-08,32.94,0,100
-1006,2024-01-08,187.04,0,44
-1007,2024-01-08,23.09,0,30
-1008,2024-01-08,155.89,1,13
-1009,2024-01-08,66.31,0,12
-1010,2024-01-08,182.28,0,9
-1011,2024-01-08,44.06,1,100
-1012,2024-01-08,21.94,0,25
-1013,2024-01-08,80.74,0,100
-1014,2024-01-08,141.87,0,44
-1015,2024-01-08,27.82,0,47
-1016,2024-01-08,30.19,0,46
-1017,2024-01-08,22.68,0,23
-1018,2024-01-08,44.2,0,39
-1019,2024-01-08,100.19,0,29
-1020,2024-01-08,129.13,0,26
-1021,2024-01-08,21.24,1,21
-1022,2024-01-08,85.71,0,4
-1023,2024-01-08,143.12,0,2
-1024,2024-01-08,72.52,0,25
-1000,2024-01-09,138.43,1,15
-1001,2024-01-09,67.57,1,90
-1002,2024-01-09,52.96,1,19
-1003,2024-01-09,61.96,0,12
-1004,2024-01-09,18.31,1,8
-1005,2024-01-09,32.94,0,83
-1006,2024-01-09,187.04,0,40
-1007,2024-01-09,23.09,0,11
-1008,2024-01-09,155.89,1,100
-1009,2024-01-09,66.31,0,100
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-1015,2024-01-09,27.82,0,35
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-1020,2024-01-09,129.13,0,7
-1021,2024-01-09,23.6,0,10
-1022,2024-01-09,85.71,0,100
-1023,2024-01-09,143.12,0,100
-1024,2024-01-09,72.52,0,25
-1000,2024-01-10,138.43,1,1
-1001,2024-01-10,67.57,1,75
-1002,2024-01-10,58.84,0,5
-1003,2024-01-10,55.76,1,1
-1004,2024-01-10,20.34,0,8
-1005,2024-01-10,32.94,0,65
-1006,2024-01-10,187.04,0,25
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-1012,2024-01-10,21.94,0,8
-1013,2024-01-10,80.74,0,82
-1014,2024-01-10,141.87,0,12
-1015,2024-01-10,27.82,0,29
-1016,2024-01-10,30.19,0,18
-1017,2024-01-10,22.68,0,7
-1018,2024-01-10,44.2,0,15
-1019,2024-01-10,100.19,0,16
-1020,2024-01-10,116.22,1,3
-1021,2024-01-10,21.24,1,100
-1022,2024-01-10,77.14,1,87
-1023,2024-01-10,128.81,1,94
-1024,2024-01-10,72.52,0,23
-1000,2024-01-11,153.81,0,100
-1001,2024-01-11,75.08,0,75
-1002,2024-01-11,58.84,0,100
-1003,2024-01-11,61.96,0,100
-1004,2024-01-11,20.34,0,100
-1005,2024-01-11,32.94,0,64
-1006,2024-01-11,187.04,0,22
-1007,2024-01-11,23.09,0,100
-1008,2024-01-11,173.21,0,81
-1009,2024-01-11,66.31,0,91
-1010,2024-01-11,182.28,0,85
-1011,2024-01-11,48.96,0,76
-1012,2024-01-11,21.94,0,7
-1013,2024-01-11,80.74,0,69
-1014,2024-01-11,141.87,0,100
-1015,2024-01-11,27.82,0,11
-1016,2024-01-11,30.19,0,17
-1017,2024-01-11,20.41,1,100
-1018,2024-01-11,44.2,0,6
-1019,2024-01-11,100.19,0,2
-1020,2024-01-11,129.13,0,100
-1021,2024-01-11,23.6,0,87
-1022,2024-01-11,85.71,0,84
-1023,2024-01-11,143.12,0,89
-1024,2024-01-11,72.52,0,11
-1000,2024-01-12,153.81,0,85
-1001,2024-01-12,75.08,0,73
-1002,2024-01-12,52.96,1,89
-1003,2024-01-12,61.96,0,93
-1004,2024-01-12,20.34,0,83
-1005,2024-01-12,32.94,0,54
-1006,2024-01-12,187.04,0,5
-1007,2024-01-12,23.09,0,99
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diff --git a/datasets/dataset-1000-com-preco-variavel-e-renovacao-estoque.csv b/datasets/dataset-1000-com-preco-variavel-e-renovacao-estoque.csv
deleted file mode 100644
index da0025b..0000000
--- a/datasets/dataset-1000-com-preco-variavel-e-renovacao-estoque.csv
+++ /dev/null
@@ -1,1001 +0,0 @@
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diff --git a/datasets/dataset-500-curso-sagemaker-canvas-dio.csv b/datasets/dataset-500-curso-sagemaker-canvas-dio.csv
deleted file mode 100644
index 066f767..0000000
--- a/datasets/dataset-500-curso-sagemaker-canvas-dio.csv
+++ /dev/null
@@ -1,501 +0,0 @@
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diff --git a/single_prediction_results (1).csv b/single_prediction_results (1).csv
new file mode 100644
index 0000000..203b084
--- /dev/null
+++ b/single_prediction_results (1).csv	
@@ -0,0 +1,11 @@
+label,probability
+P001,0.008425393141806126
+P002,0.004763222299516201
+P003,0.8833695650100708
+P004,0.013247296214103699
+P005,0.014231186360120773
+P006,0.0008298978791572154
+P007,0.0645875483751297
+P008,0.0036239188630133867
+P009,0.0012158494209870696
+P010,0.005706127732992172
\ No newline at end of file
diff --git a/single_prediction_results (2).csv b/single_prediction_results (2).csv
new file mode 100644
index 0000000..203b084
--- /dev/null
+++ b/single_prediction_results (2).csv	
@@ -0,0 +1,11 @@
+label,probability
+P001,0.008425393141806126
+P002,0.004763222299516201
+P003,0.8833695650100708
+P004,0.013247296214103699
+P005,0.014231186360120773
+P006,0.0008298978791572154
+P007,0.0645875483751297
+P008,0.0036239188630133867
+P009,0.0012158494209870696
+P010,0.005706127732992172
\ No newline at end of file
diff --git a/single_prediction_results.csv b/single_prediction_results.csv
new file mode 100644
index 0000000..ed35e3a
--- /dev/null
+++ b/single_prediction_results.csv
@@ -0,0 +1,11 @@
+label,probability
+P001,0.016922136768698692
+P002,0.0033688561525195837
+P003,0.016920343041419983
+P004,0.5135716795921326
+P005,0.01887904852628708
+P006,0.014528182335197926
+P007,0.023847635835409164
+P008,0.30723679065704346
+P009,0.00792734045535326
+P010,0.07679799944162369
\ No newline at end of file