Project done!

.gitignore

@@ -0,0 +1,141 @@
+# Created by https://www.gitignore.io/api/macos,pycharm,jupyternotebook,visualstudiocode,jupyternotebooks
+# Edit at https://www.gitignore.io/?templates=macos,pycharm,jupyternotebook,visualstudiocode,jupyternotebooks
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+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
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+### JupyterNotebooks ###
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
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+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
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+### macOS ###
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+# End of https://www.gitignore.io/api/macos,pycharm,jupyternotebook,visualstudiocode,jupyternotebooks

your-project/README.md

@@ -1,72 +1,53 @@
 <img src="https://bit.ly/2VnXWr2" alt="Ironhack Logo" width="100"/>
 
-# Title of My Project
-*[Your Name]*
+# Rossman - Sales Forecast
+*[Faisal Hammad*
 
-*[Your Cohort, Campus & Date]*
+*[DAFT-RMT-MAR21]*
 
 ## Content
 - [Project Description](#project-description)
 - [Hypotheses / Questions](#hypotheses-questions)
 - [Dataset](#dataset)
 - [Cleaning](#cleaning)
-- [Analysis](#analysis)
-- [Model Training and Evaluation](#model-training-and-evaluation)
+- [Analysis](#analysis_model-training_and_evaluation)
 - [Conclusion](#conclusion)
 - [Future Work](#future-work)
-- [Workflow](#workflow)
-- [Organization](#organization)
 - [Links](#links)
 
 ## Project Description
-Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show.
+This project aims to create a timeseries forecast to predict sales based on historical sales data for 1,115 Rossmann stores. Note that some stores in the dataset were temporarily closed for refurbishment.
+
 
 ## Hypotheses / Questions
-* What data/business/research/personal question you would like to answer?
-* What is the context for the question and the possible scientific or business application?
-* What are the hypotheses you would like to test in order to answer your question?  
-Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required.
+Rossman closed nearly 200 stores for a period of six months for renovation. Does that affect the model negatively?
+* if yes, How can we create a model that would predict the sales after they opened?
 
 ## Dataset
-* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset.
-* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo.
-* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary.
-* If the question cannot be answered with the available data, why not? What data would you need to answer it better?
+The data & Data Description is available here: https://www.kaggle.com/c/rossmann-store-sales/data
+
 
 ## Cleaning
-Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process.
+Data was relatively clean since in a timeseries, we only need a few columns.
+I created some functions that aggregated month data or helped in the preprocessing.
 
-## Analysis
-* Overview the general steps you went through to analyze your data in order to test your hypothesis.
-* Document each step of your data exploration and analysis.
-* Include charts to demonstrate the effect of your work.
-* If you used Machine Learning in your final project, describe your feature selection process.
+## Analysis, Model Training, and Evaluation
+* Plotting data
+* Inspected holidays
+* Created fb Prophet models, check residuals, iterate, check again.. etc
+* Created for-loops to hypertune models, and used Auto-Arima** from the pmdarima Library for checking residual RSME
 
-## Model Training and Evaluation
-*Include this section only if you chose to include ML in your project.*
-* Describe how you trained your model, the results you obtained, and how you evaluated those results.
 
 ## Conclusion
 * Summarize your results. What do they mean?
 * What can you say about your hypotheses?
 * Interpret your findings in terms of the questions you try to answer.
 
 ## Future Work
-Address any questions you were unable to answer, or any next steps or future extensions to your project.
-
-## Workflow
-Outline the workflow you used in your project. What were the steps?
-How did you test the accuracy of your analysis and/or machine learning algorithm?
+- Create a model for each store / or bins of stores.
+- Use competitor data for a complementary model.
 
-## Organization
-How did you organize your work? Did you use any tools like a trello or kanban board?
-
-What does your repository look like? Explain your folder and file structure.
 
 ## Links
-Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project.
-
-
-[Repository](https://github.com/)  
-[Slides](https://slides.com/)  
-[Trello](https://trello.com/en)  
+[Repository](https://github.com/Faisal7ammad/Project-Week-8-Final-Project)  
+[Slides](https://docs.google.com/presentation/d/1wk9iASfi8rPyScNvLtEniumgv6QKyLzAJ3LGOIeespM/edit?usp=sharing)