213 changed files with 68 additions and 115920 deletions
--- a/.gitignore
+++ b/.gitignore
@ -1,2 +1 @@
-venv/
+venv/
 .obsidian
--- a/README.md
+++ b/README.md
@ -1,22 +0,0 @@
 # Applied Optimization and Machine Learning
 This repo contains all the materials I use while giving the course Applied
 Optimization and Machine Learning techniques and its sibling Practical Data
 Science for Operations Management during year 2022/2023.
 It contains the following materials:
 - Lecture notes: the different notes I used in the classes.
 - Cases: the three cases that students had to do during the course. Each case
  includes a narrative description, data and some helping code. The folders
  also contain the grading details.
 - Exam: the exam, along with the grading helper to calculate the final scores
  of students.
 - Other: a bit of everything.
    - Random notes
    - Memories I want to keep.
    - Interesting materials that I did not directly use during the course.
    - Ideas for next year.
 The repository also contains all files from previous editions in the `archive`
 folder.
--- a/panel.md
+++ b/panel.md
@ -1,17 +0,0 @@
 | Week | Ready | Main item   |  S   | Classes                                                                                                                                                                | Student work                                                                                                                                                                                                                                                                                                |     |     |
 | ---- | ----- | ----------- | --- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | --- |
 | 1    | Yes   | Python Prep |  N   | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies                                                  | - Python prep                                                                                                                                                                                                                                                                                               |     |     |
 | 2    |   Yes    |    Python Prep         |  N   | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1.                 | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1.                                           |     |     |
 | 3    |       | Case 1            |  N   | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics)                                                                                                              |     |     |
 | 4    |       | Case 1            |  Y   | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1                                                            | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |     |     |
 | 5    |       | Case 1/2            |  Y  | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2                                                        | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html)  |     |     |
 | 6    |       | Case 2            |  Y  | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2                        | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf)                                                                                                                                                                       |     |     |
 | 7    |       | Case 2/3            |  Y  | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2                                 | - Work on case 2 <br/> - Read case 3                                                                                                                                                                                                                                                                        |     |     |
 | 8    |       | Case 3            |  Y  | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3                                         | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)                                                                                                            |     |     |
 | 9    |       | Case 3            |  Y  | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3                              | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0)                                                                                                                                                                                        |     |     |
 | 10   |       | Case 3            |  N  | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation                                                              | - Work on case 3                                                                                                                                                                                                                                                                                            |     |     |
 | 11   |       |             |     | - Exam                                                                                                                                                                 | - Deliver case 3                                                                                                                                                                                                                                                                                            |     |     |
--- a/aot/cases/case_1/case_1.zip
+++ b/aot/cases/case_1/case_1.zip
--- a/aot/cases/case_1/case_1_description.docx
+++ b/aot/cases/case_1/case_1_description.docx
--- a/aot/cases/case_1/case_1_description.pdf
+++ b/aot/cases/case_1/case_1_description.pdf
--- a/aot/cases/case_1/case_1_student_notebook.ipynb
+++ b/aot/cases/case_1/case_1_student_notebook.ipynb
@ -1,405 +0,0 @@
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to PuLP\n",
        "\n",
        "For case 1, you will need to define and solve optimization problems. In this notebook, I'll help you understand how to use `pulp`, a Python package for modeling optimization problems. You might want to check the following links:\n",
        "\n",
        "- Documentation: https://coin-or.github.io/pulp/\n",
        "- Homepage: https://github.com/coin-or/pulp\n",
        "\n"
      ],
      "metadata": {
        "id": "eLvjUuJdzS7z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing and checking all is in place"
      ],
      "metadata": {
        "id": "HFavOEVS0dbY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first thing you need to do is to install `pulp`. `pulp` is not in the standard available packages in Colab, so you need to run the following cell once. "
      ],
      "metadata": {
        "id": "HgZwpjUG0PsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pulp"
      ],
      "metadata": {
        "id": "ni6Q_YiO0nIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After doing that, you can import the library."
      ],
      "metadata": {
        "id": "k9YI0Kzw0qLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pulp"
      ],
      "metadata": {
        "id": "hw6keX7x0tZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If all is good, running the following command will print a large log testing `pulp`. The last line should read \"OK\"."
      ],
      "metadata": {
        "id": "vD_rXehL1KXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pulp.pulpTestAll()"
      ],
      "metadata": {
        "id": "Ney2a8mu1JqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining and solving problems\n",
        "\n",
        "The following cells show you the absolute minimum to model and solve a problem with `pulp`. The steps are:\n",
        "\n",
        "1. Define decision variables\n",
        "2. Define the target function\n",
        "3. Define the constraints\n",
        "4. Assemble the problem\n",
        "5. Solve it\n",
        "6. Examine results\n",
        "\n",
        "For more flexibility, options and interesting stuff, please check up the PuLP documentation."
      ],
      "metadata": {
        "id": "oiXz40NR1whf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define decision variables"
      ],
      "metadata": {
        "id": "nq5bcQs03g0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = pulp.LpVariable(\n",
        "    name=\"x\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "y = pulp.LpVariable(\n",
        "    name=\"y\",\n",
        "    cat=pulp.LpInteger # This will make the variable integer only\n",
        "    )\n",
        "\n",
        "z = pulp.LpVariable(\n",
        "    name=\"z\",\n",
        "    cat=pulp.LpBinary # This will make the variable binary (only 0 or 1)\n",
        ")"
      ],
      "metadata": {
        "id": "0SPhww4L3buh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define the target function"
      ],
      "metadata": {
        "id": "uhlbq2oO35kp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 10 * x - 5 * y + z"
      ],
      "metadata": {
        "id": "pu3Im9DH39CN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define constraints"
      ],
      "metadata": {
        "id": "lqD0dD474Izw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "constraint_1 = x >= 0\n",
        "constraint_2 = y >= 0\n",
        "constraint_3 = x >= 10\n",
        "constraint_4 = y <= 50"
      ],
      "metadata": {
        "id": "5Cu51lYj4OUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assemble the problem\n",
        "\n",
        "To put all the parts together, you need to declare a problem and specify if you want to minimize or maximize the target function.\n",
        "\n",
        "Once you have that:\n",
        "- First, you \"add\" the target function.\n",
        "- After, you \"add\" all the constraints you want to include."
      ],
      "metadata": {
        "id": "d5nq94IM4kSU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"my_silly_problem\", pulp.LpMinimize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    constraint_1,\n",
        "    constraint_2,\n",
        "    constraint_3,\n",
        "    constraint_4\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "yI-Oiwh64mRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Solve it\n",
        "\n",
        "The problem object is now unsolved. You can call the `solve` method on it to find a solution."
      ],
      "metadata": {
        "id": "RJTWfR8-5fBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()"
      ],
      "metadata": {
        "id": "4Fbltpbp5mRi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine results\n",
        "\n",
        "After calling `solve` on a problem, you can access:\n",
        "- The status of the problem. It can be solved, but also it might show to be not feasible.\n",
        "- The values assigned to each decision variable.\n",
        "- The final value for the target function.\n",
        "\n"
      ],
      "metadata": {
        "id": "0pc9RmrO7FKo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(pulp.value(problem.objective))"
      ],
      "metadata": {
        "id": "8U4xVvUg9W07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Peanut Butter Example\n",
        "\n",
        "As an additional example, you can find below the model and solver for the Peanut Butter Sandwich example we discussed on our lectures."
      ],
      "metadata": {
        "id": "I2lNaFm2XVK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pb = pulp.LpVariable(\n",
        "    name=\"Peanut Butter grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "b = pulp.LpVariable(\n",
        "    name=\"Bread grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )"
      ],
      "metadata": {
        "id": "HI4E2dNoXVK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 5.88 * pb + 2.87 * b"
      ],
      "metadata": {
        "id": "PfTxq8R0XVLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_negative_pb = pb >= 0\n",
        "no_negative_b = b >= 0\n",
        "max_pb_we_have = pb <= 200\n",
        "max_b_we_have = b <= 300\n",
        "doctors_dietary_restriction = pb <= 0.13 * b"
      ],
      "metadata": {
        "id": "2X1AzQM8XVLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"sandwich_problem\", pulp.LpMaximize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    no_negative_pb,\n",
        "    no_negative_b,\n",
        "    max_pb_we_have,\n",
        "    max_b_we_have,\n",
        "    doctors_dietary_restriction\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "3oEoQXebXVLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()\n",
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(f\"Final calories: {pulp.value(problem.objective)}\")"
      ],
      "metadata": {
        "id": "u1vI73kiXVLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case 2\n",
        "\n",
        "You can use the rest of the notebook to work on the different parts of case 1."
      ],
      "metadata": {
        "id": "6kWgbTjU-LaN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Good luck!"
      ],
      "metadata": {
        "id": "aYzseTWh-Sal"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
 }
--- a/aot/cases/case_1/grading/case_1_grading.xlsx
+++ b/aot/cases/case_1/grading/case_1_grading.xlsx
--- a/aot/cases/case_1/grading/grading_team_1.xlsx
+++ b/aot/cases/case_1/grading/grading_team_1.xlsx
--- a/aot/cases/case_1/grading/grading_team_10.xlsx
+++ b/aot/cases/case_1/grading/grading_team_10.xlsx
--- a/aot/cases/case_1/grading/grading_team_11.xlsx
+++ b/aot/cases/case_1/grading/grading_team_11.xlsx
--- a/aot/cases/case_1/grading/grading_team_12.xlsx
+++ b/aot/cases/case_1/grading/grading_team_12.xlsx
--- a/aot/cases/case_1/grading/grading_team_2.xlsx
+++ b/aot/cases/case_1/grading/grading_team_2.xlsx
--- a/aot/cases/case_1/grading/grading_team_3.xlsx
+++ b/aot/cases/case_1/grading/grading_team_3.xlsx
--- a/aot/cases/case_1/grading/grading_team_5.xlsx
+++ b/aot/cases/case_1/grading/grading_team_5.xlsx
--- a/aot/cases/case_1/grading/grading_team_6.xlsx
+++ b/aot/cases/case_1/grading/grading_team_6.xlsx
--- a/aot/cases/case_1/grading/grading_team_8.xlsx
+++ b/aot/cases/case_1/grading/grading_team_8.xlsx
--- a/aot/cases/case_1/grading/grading_team_9.xlsx
+++ b/aot/cases/case_1/grading/grading_team_9.xlsx
--- a/aot/cases/case_2/.Rhistory
+++ b/aot/cases/case_2/.Rhistory
--- a/aot/cases/case_2/case2_student_notebook.ipynb
+++ b/aot/cases/case_2/case2_student_notebook.ipynb
--- a/aot/cases/case_2/case_2.zip
+++ b/aot/cases/case_2/case_2.zip
--- a/aot/cases/case_2/case_2_description.docx
+++ b/aot/cases/case_2/case_2_description.docx
--- a/aot/cases/case_2/case_2_description.md
+++ b/aot/cases/case_2/case_2_description.md
@ -1,196 +0,0 @@
 # Case 1: Simulating Stock Policies
 - Title: Choosing stock policies under uncertainty
 - Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Elisa, the
  regional manager for the italian region, is not happy with their inventory
  policies for raw beans. The students are asked to analyse the problems posed
  by Elisa and apply simulation techniques, together with real data, to
  recommend a stock policy for the company's warehouse in the italian region.
  Python notebooks with some helpful prepared functions are provided to the
  students. The final delivery is a report with their recommendation to the
  client company, along with the used code.
 Stuff I want them to understand:
 - The model/hypothesis/validate
 - That in a simulation you set parameters, and you observe results
 - To write in a problem-solving manner.
 - That there are trade-offs and it's not trivial to find optimal solutions.
 Elements of the simulation:
 - Demand behavior
 - Lead time and standard deviation of provider (or providers)
 - Service level
 - Punishment for sales lost
 Observable effects of policies:
 - Mean inventory at hand
 - Service level
 - Warehousing/Capital Cost
 - Lost sales cost
 # Case 1: Choosing ordering goods under uncertainty
 You are part of an expert simulation team in SimiUPF SL. You have been assigned
 to a new project with a client company, Beanie Limited. Beanie Limited is a
 coffee roasting company and also distributes raw coffee beans through Europe
 and Middle East.
 Specifically, you will be working for Elisa Bolzano, the Director of Beanie
 Limited's warehouse located in Caserta, near Naples. Elisa is the full
 responsible for all the operations in the warehouse. She has requested the help
 of the SimiUPF team because she is worried about how certain things are managed
 in the warehouse and wants your help.
 The Caserta warehouse serves the raw coffee beans distribution business of
 Beanie Limited in southern europe and the mediterranean. The warehouse and its
 team are responsible for serving clients and also other smaller regional
 warehouses from Beanie Limited in this geography. From the warehouse point of
 view, they are usually just called "the clients". Whenever one of the clients
 needs raw beans, they arrange a transport truck that goes to the warehouse to
 pick up a certain amount of goods. Elisa's team fills up the truck with the
 requested goods, and then the clients take care of receiving that at their own
 locations. Given the size and relevance of the Caserta warehouse, activity is
 pretty much constant, with goods leaving the warehouse towards client locations
 every day.
 The Caserta warehouse itself has only one way to source coffee beans to store
 in their warehouse: requesting them to the Beanie Limited central offices in
 Diemen, near Amsterdam. Whenever Elisa's team considers that more stock is
 needed, they post a Purchase Order to the central office for a certain amount
 of beans. The central office arranges the goods and the delivery and, after a
 few days, the goods reach Caserta and are stored in the warehouse. The central
 office tries to ensure a lead time of 7 days (lead time is the time that passes
 between an order being placed and the goods reaching their destination), but
 the reality is they do what they can and this time is not always respected.
 Stock is a necessary evil (it implies a lot of cost), but Elisa's warehouse
 needs it to serve the clients in their region properly. Having too little stock
 means the clients need to wait long times to get their goods, which is risky
 for the business. On the other hand, having a lot of stock means high warehouse
 costs and financial opportunity cost (if Beanie Limited has 1 million € in
 coffee beans in a warehouse, that is 1 million € they can't invest somewhere
 else to improve their business). Thus, Elisa needs the stock to be as small as
 possible, while making sure she is not disappointing clients at the same time
 because of stockouts.
 Elisa is calling you because 2021 was a terrible year for the warehouse. The
 year was a chaotic one, and Elisa's team was not able to run operations
 smoothly. Although Elisa is not providing exact numbers, she is very well aware
 that the warehouse stock was unnecessarily high at times, and that there were
 too many periods where the warehouse was out of stock and clients had to wait
 to get their goods.
 Elisa thinks the main reason for this is the lack of a clear policy for when to
 order and how much to order from Diemen. Her team decides independently when to
 do it, and Elisa has a feeling that they are not approaching these decisions
 the right way. This means that sometimes they order when there is no need to,
 sometimes they don't order when they should, and that the amounts being ordered
 might not always be the optimal ones.
 Here is where you come in. As simulation experts, Elisa expects from you that
 you can help design an ordering policy to fix these issues. Doing this implies
 examining data from last year, building a proper simulation to examine the
 different factors being involved, and deciding when and how should Elisa's team
 order more goods from Diemen.
 Elisa expects a report where you share your findings and recommendations in a
 clear way that can help her team. Also, Elisa does not trust you blindly: you
 need to motivate the reasoning behind your recommendations. Otherwise, she will
 not feel comfortable implementing your recommendations and the bosses at
 SimiUPF will be mad at you...
 ## Detailed task definition
 - Below you will find four levels of questions. Levels 1 to 3 are compulsory.
  Level 4 is optional.
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Elisa, should give
  her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report and code.
 - You need to use a Python notebook to solve all levels. A helper notebook is
  provided. Please attach a notebook that shows your
  solution/proposal/analysis.
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 - You are provided with three tables that contain real data from 2021.
    - demand_events: this table shows how many beans left the Caserta warehouse
      to serve clients. There is some amount leaving every day because the
      warehouse serves many small orders from small clients, so there is always
      some order being fulfilled. The amount is measured in kilograms, and
      represents the total amount that left during that day. Individual orders
      are not relevant for this case, so we only look at daily total figures.
    - sourcing_events: this table shows the Purchase Orders that Elisa's team
      placed to Diemen. For each order, there are two dates: the date when
      Elisa's team placed the order, and the date where the beans actually
      reached the Caserta warehouse. The difference between those dates is the
      real lead time of each order. The amount is measured in kilograms.
    - stock_state: this table shows the stock at the warehouse at the end of
      each date. As you can guess, the stock for a certain date is the stock of
      the previous day, plus the goods that reached Caserta coming from Diemen,
      minus the goods that left the warehouse to serve client orders. A
      negative stock is not a challenge to the laws of physics: it means
      clients are waiting for their requested beans. If one row shows -1.000,
      it means that the warehouse is empty, and clients are awaiting a total
      amount of 1.000 kgs of beans. If next morning, a 1.000 kgs reach Caserta
      from Diemen, those will be used immediately to satisfy those awaiting
      clients, and the warehouse stock will become 0.
 ## Notebook
 A notebook with some helping code has been provided. The code contains a small
 simulation engine that can help you simulate a year of activity for the
 warehouse. The instructions on how to use the code are in the notebook itself.
 ## Levels
 - Level 1
    - Elisa wants you to measure the performance of the last year, providing
      quantitative metrics. She knows it was a bad year, but hasn't looked at
      the real data to summarize how bad it was. Remember that there is a
      trade-off: too much stock, is not desired, but running out of stock and
      making clients wait is also negative.
    - Going one step further, Elisa wants to know: what was done wrong?
 - Level 2
    - Elisa wants you to propose an ordering policy. This means, that you need
      to define a rule that, once each day, should answer the questions: should
      we place a Purchase Order to request beans from Diemen today? If yes, how
      much should we order?
    - Use simulation to present metrics on what is the expected performance
      with the policy you are proposing. Remember, you need to convince Elisa
      that this is better than what happens today.
    - As a specific constraint, Elisa explains that she wants that the
      probability of a stockout to be at most of 5% on any given day.
 - Level 3
    - Right after you finished designing your policy for level 2, Elisa called
      with some news: she has just been informed by the management in Diemen
      that a new Minimum Order Quantity (MOQ) rule will begin soon. This rule
      means that, when the Caserta warehouse places an order to request
      material from Diemen, the order should be of at least 500,000 kgs of
      beans, and not less than that.
    - Elisa wants you to take this into account. Does it affect the policy you
      proposed for level 2? If so, you need to come up with a new one that
      adapts to this rule and compare it to the previous one.
 - Level 4
    - Elisa briefly discussed with you in one meeting that there is an option
      to come to an agreement with the team in Diemen to improve the lead time
      stability. The proposal from Diemen is that, if the target lead time was
      set to something higher that the current 7 days target, providing a more
      stable delivery would be feasible.
    - The specific proposal from Diemen is: if the lead time target is changed
      to 15 days, they provide a 100% guarantee that orders will be delivered
      in exactly 15 days.
    - Elisa would love if you could take some additional time to study this
      proposal. What is better for Caserta? The current 7 days target
      lead-time, with unstable delivery times? Or a fixed, 15-day lead time?
    - The MOQ rule of level 3 still applies.
--- a/aot/cases/case_2/case_2_description.pdf
+++ b/aot/cases/case_2/case_2_description.pdf
--- a/aot/cases/case_2/good_examples/Report_case2_Team#1.docx
+++ b/aot/cases/case_2/good_examples/Report_case2_Team#1.docx
--- a/aot/cases/case_2/good_examples/TEAM
+++ b/aot/cases/case_2/good_examples/TEAM
--- a/aot/cases/case_2/grading/case_2_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_10_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_10_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_11_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_11_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_12_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_12_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_1_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_1_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_2_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_2_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_3_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_3_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_5_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_5_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_5_grading_review_reply.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_5_grading_review_reply.xlsx
--- a/aot/cases/case_2/grading/case_2_team_6_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_6_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_8_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_8_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_9_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_9_grading.xlsx
--- a/aot/cases/case_3/case_3.zip
+++ b/aot/cases/case_3/case_3.zip
--- a/aot/cases/case_3/case_3_description.docx
+++ b/aot/cases/case_3/case_3_description.docx
--- a/aot/cases/case_3/case_3_description.md
+++ b/aot/cases/case_3/case_3_description.md
@ -1,175 +0,0 @@
 # Case 3: Improving last mile logistics with Machine Learning
 After your last engagement, you have pretty much become the go-to
 service provider for advanced quantitative methods. Congratulations!
 You have been called again to help a different manager within Beanie Limited:
 Estefania Pelaez. Estefania is the city manager for Barcelona. She is in charge
 of all commercial and logistic operations that happen in the city.
 One of the operations that Beanie Limited runs in Barcelona is their own
 last-mile coffee delivery service. The company runs a small fleet of vans and
 trucks that delivers small orders of roasted coffee beans (typically,
 around 10-100kg of coffee per delivery) to restaurants, cafes, hotels and other
 businesses in the city.
 The efficiency of the deliveries is important to keep margins profitable for
 Beanie Limited. A sloppy management can make the company lose money. Hence,
 Estefania is always working on ways to make the operations as smooth as
 possible.
 Currently, Beanie Limited has rented space in two warehouses: one located in
 Zona Franca and another one in Baro de Viver. Complementing that, the company
 has a small fleet of combi vans, regular sized vans and one truck, which are
 used by Beanie Limited own drivers to deliver the coffee beans from the
 warehouses to the customer's facilities.
 Orders placed by the customers are predictable and placed with time in advance,
 which allows Estefania and her team to plan the deliveries and minimize wasted
 effort by the fleet. Since they know which locations they will need to deliver
 to, they use a routing software that drafts the routes that each vehicle will
 cover each day.
 Recently, Estefania recently realized something: deliveries are almost always
 taking place too early or too late. After researching with some data, Estefania
 found out that there was nothing wrong with the routing software time
 estimates: the driving time between locations predicted by the software is
 accurate. The real issue is related to what Estefania's team calls the "
 engine-off" time.
 The engine-off time is the time a driver spends actually dropping off goods in
 a client location. It's called engine-off because the clock starts ticking when
 the driver takes the keys off the van and stops when the driver starts driving
 again.
 Currently, Estefania and her team assume an engine-off time of 3 minutes for
 all deliveries when building the delivery routes and schedules. But it seems
 that this not realistic at all and is causing a lot of trouble with the
 schedules. Clients are not happy with delivery times not being respected, some
 driver routes end up too early (which means that the same driver could have
 covered more clients) and some others run for too long (which means they have
 to go back to the warehouse without delivering all the goods requested by the
 clients).
 If Estefania could know beforehand what would be the engine-off time of
 different deliveries, she could improve the route planning to fix all of these
 issues. She has been told that Machine Learning could help with this and is
 expecting you to find out if and how it can be applied to this problem.
 ## Detailed Task Definition
 - Below you will find tasks organized in four levels. 
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Estefania, should
  give her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report.
 - You need to use a Python notebook to solve all levels. Please attach a
  notebook that shows your solution/proposal/analysis. Your notebook should be
  runnable "as-is". That means that anyone should be able to run it from
  beginning to end without any additional instructions or action required (
  except for uploading data from a CSV in the Google Colab environment. That
  requires someone to upload the file with a few clicks and it's fine).
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 By joining the customer database together with past deliveries details,
 Estefania has built a dataset of execute deliveries. The table contains 9,000
 examples of past deliveries and their engine-off times. The exact field
 meanings are explained below:
 - client_name: the name of the client.
 - truck_size: what type of truck was being used. Can be one of Combi, Van or
  Truck.
 - truck_origin_warehouse: from which Beanie Limited warehouse did the route
  start.
 - delivery_timestamp: at what date and time was the delivery done (defined as
  the moment the engine-off time starts).
 - total_weight: total weight of the goods delivery.
 - brand_1_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #1.
 - brand_2_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #2.
 - brand_3_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #3.
 - driver_id: the ID of the driver that was driving the route.
 - is_fresh_client: whether the client was fresh at the date of the delivery.
  Fresh clients are clients that have been doing business with Beanie for less
  than 30 days.
 - postcode: the postcode of the client location.
 - business_category: whether the client is a hotel, a cafe or restaurant or a
  coffee retailer.
 - floor: the physical position of the client location.
 - partnership_level: indicates the partnership level with Beanie. Key Account
  are important clients for Beanie Limited. Diamond clients are the top
  priority clients for the company.
 - box_count: how many distinct boxes were delivered to the client. The coffee
  beans bags are grouped into boxes for delivery.
 - final_time: the engine-off time, measured in seconds.
 ## Notebook
 Case 3 comes with no helping notebook: this time, you will have to code things
 from scratch yourselves. Remember that you are still suposed to write and
 deliver a notebook (see the "Detailed Task Definition" section).
 A few comments on your notebook:
 - I'm a going to constraint you to use [scikit-learn](https://scikit-learn.org/stable/) as a ML library. You can
  of course use other useful Python libraries such as pandas, numpy, etc. But
  for ML modeling, please go with scikit-learn.
 - Below you can find some useful materials which relate to what you need to do
  as part of the case:
    - [A simple, guided EDA on the Titanic Dataset](https://www.datacamp.com/tutorial/kaggle-machine-learning-eda)
    - [A guide on regression performance metrics](https://machinelearningmastery.com/regression-metrics-for-machine-learning/)
      and some [material from scikit-learn on the same topic](https://scikit-learn.org/stable/modules/classes.html#regression-metrics)
    - An [introduction to cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/)
    - A thorough [review on why we need to use baselines](https://blog.ml.cmu.edu/2020/08/31/3-baselines/) in ML
    - A simple [introduction to linear regression with scikit-learn](https://stackabuse.com/linear-regression-in-python-with-scikit-learn/)
 ## Levels
 ### Level 1
 - Assess for Estefania if ML is a good choice for her problem and explain why.
 - Perform Exploratory Data Analysis on the given data. Is it clean? Which
  variables could be useful to explain the engine-off time? Are there any other
  interesting things you can draw from the dataset?
 ### Level 2
 - Present how are you going to measure performance for this problem and how you
  will use the available data for testing it.
 - Develop a baseline algorithm and evaluate its performance.
 ### Level 3
 - Develop the best model you can make to predict engine-off time.
 - Explain your methodology and report on performance.
 - Compare your performance to the baseline algorithm. Reflect on what is the
  cause of whatever differences can be observed between both.
 ### Level 4
 After presenting your model and results, Estefania has two different questions:
 - Estefania would like to learn from the ML algorithm. What are the most
  relevant features that define the engine-off time? Can you somehow quantify
  how important each is or which are most useful?
 - Estefania is interested in learning about next steps. What can be done to
  improve even more the model performance and achieve better results?
 ### SPECIAL
 For this case, we are going to run a little competition. There will be a
 surprise gift on the last lecture for the team that wins.
 The competition consists on getting the best performing model of the course. I
 have a hidden part of Estefania's dataset. Besides the data for the case, I have
 also added an additional thousand records for which you don't have the engine off 
 time (so you can make predictions, but you can't compare them against the true 
 result). 
 To enter the competition, you will have to send me a csv with a single column named "predictions". Each row should be a record of the extra dataset, delivered in the same order as the predictive features you received. I will measure the RMSE of all deliveries and the team with the lowest RMSE will be the winner.
--- a/aot/cases/case_3/case_3_description.pdf
+++ b/aot/cases/case_3/case_3_description.pdf
--- a/aot/cases/case_3/competition/beanie_limited_predictions.csv
+++ b/aot/cases/case_3/competition/beanie_limited_predictions.csv
--- a/aot/cases/case_3/competition/ground_truth.csv
+++ b/aot/cases/case_3/competition/ground_truth.csv
--- a/aot/cases/case_3/competition/results.txt
+++ b/aot/cases/case_3/competition/results.txt
@ -1,7 +0,0 @@
 METRIC: RMSE
 team_12 RMSE: 96.45072068592552
 team_2 RMSE: 112.43239178382082
 team_3 RMSE: 140.60987164535905
 team_1 RMSE: 156.38651077473006
 team_5 RMSE: 159.27300996653568
 beanie_limited RMSE: 396.84362604059373
--- a/aot/cases/case_3/competition/team_12_predictions.csv
+++ b/aot/cases/case_3/competition/team_12_predictions.csv
--- a/aot/cases/case_3/competition/team_1_predictions.csv
+++ b/aot/cases/case_3/competition/team_1_predictions.csv
--- a/aot/cases/case_3/competition/team_2_predictions.csv
+++ b/aot/cases/case_3/competition/team_2_predictions.csv
--- a/aot/cases/case_3/competition/team_3_predictions.csv
+++ b/aot/cases/case_3/competition/team_3_predictions.csv
--- a/aot/cases/case_3/competition/team_5_predictions.csv
+++ b/aot/cases/case_3/competition/team_5_predictions.csv
--- a/aot/cases/case_3/competition_dropoffs_df.csv
+++ b/aot/cases/case_3/competition_dropoffs_df.csv
--- a/aot/cases/case_3/competition_dropoffs_df_only_x.csv
+++ b/aot/cases/case_3/competition_dropoffs_df_only_x.csv
--- a/aot/cases/case_3/dropoffs_df.csv
+++ b/aot/cases/case_3/dropoffs_df.csv
--- a/aot/cases/case_3/full_dropoffs_df.csv
+++ b/aot/cases/case_3/full_dropoffs_df.csv
--- a/aot/cases/case_3/grading/grading.xlsx
+++ b/aot/cases/case_3/grading/grading.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_1.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_1.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_10.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_10.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_11.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_11.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_12.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_12.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_2.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_2.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_3.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_3.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_5.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_5.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_6.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_6.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_8.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_8.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_9.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_9.xlsx
--- a/aot/exam/exam.odt
+++ b/aot/exam/exam.odt
--- a/aot/exam/exam.pdf
+++ b/aot/exam/exam.pdf
--- a/aot/exam/grading.ods
+++ b/aot/exam/grading.ods
--- a/aot/exam/retake_exam.odt
+++ b/aot/exam/retake_exam.odt
--- a/aot/exam/retake_exam.pdf
+++ b/aot/exam/retake_exam.pdf
--- a/archive/2122/cases/case_1/case1_student_notebook.ipynb
+++ b/archive/2122/cases/case_1/case1_student_notebook.ipynb
--- a/archive/2122/cases/case_1/case_1.zip
+++ b/archive/2122/cases/case_1/case_1.zip
--- a/archive/2122/cases/case_1/case_1_description.docx
+++ b/archive/2122/cases/case_1/case_1_description.docx
--- a/archive/2122/cases/case_1/case_1_description.pdf
+++ b/archive/2122/cases/case_1/case_1_description.pdf
--- a/archive/2122/cases/case_1/demand_events.csv
+++ b/archive/2122/cases/case_1/demand_events.csv
@ -1,366 +0,0 @@
 date,demand_quantity
 2021-01-01,54609.49281314914
 2021-01-02,36208.63648649295
 2021-01-03,77784.17276763407
 2021-01-04,76481.81360421646
 2021-01-05,52305.87658918292
 2021-01-06,57098.56436860317
 2021-01-07,41565.68706138541
 2021-01-08,81995.500619844
 2021-01-09,71041.91466404148
 2021-01-10,31787.17080818402
 2021-01-11,32735.09633866546
 2021-01-12,32855.44553254065
 2021-01-13,55420.934082626205
 2021-01-14,48883.311263507494
 2021-01-15,48368.597773147136
 2021-01-16,40225.99478591274
 2021-01-17,69003.66723779934
 2021-01-18,67378.93368511106
 2021-01-19,59444.432628854185
 2021-01-20,54441.80415596864
 2021-01-21,52796.814721541414
 2021-01-22,30193.150803735854
 2021-01-23,62328.53756562836
 2021-01-24,43690.320158519615
 2021-01-25,78451.89473980921
 2021-01-26,47794.13927746792
 2021-01-27,51454.93947489077
 2021-01-28,64633.17690683539
 2021-01-29,67371.66310250101
 2021-01-30,51137.068372905895
 2021-01-31,62192.931782584405
 2021-02-01,62381.245234820446
 2021-02-02,62744.03145531537
 2021-02-03,51305.706023572566
 2021-02-04,52618.66719247759
 2021-02-05,51961.10865929137
 2021-02-06,55154.27434352692
 2021-02-07,39799.62917632264
 2021-02-08,65486.97890826721
 2021-02-09,55355.23228947571
 2021-02-10,46211.4777291026
 2021-02-11,53132.95392507133
 2021-02-12,31537.03525349067
 2021-02-13,46447.72089889987
 2021-02-14,63731.03176553111
 2021-02-15,54454.77009849779
 2021-02-16,40659.50720269109
 2021-02-17,63493.99813149876
 2021-02-18,54931.266644895266
 2021-02-19,28278.734877540137
 2021-02-20,36379.638867181835
 2021-02-21,62202.758260545044
 2021-02-22,54523.210135004185
 2021-02-23,42395.85236943305
 2021-02-24,42934.425415725156
 2021-02-25,51494.77047631462
 2021-02-26,44220.2960470736
 2021-02-27,45670.120416197926
 2021-02-28,57107.49381367681
 2021-03-01,28972.234058115788
 2021-03-02,43048.734607813065
 2021-03-03,41505.53405595842
 2021-03-04,47926.03548243223
 2021-03-05,61278.99549030161
 2021-03-06,39044.50052424295
 2021-03-07,37142.63665375576
 2021-03-08,59385.01021647509
 2021-03-09,19622.861200135892
 2021-03-10,42875.82033258566
 2021-03-11,37298.094228973925
 2021-03-12,53411.899019061944
 2021-03-13,45345.99865109816
 2021-03-14,53211.40616195306
 2021-03-15,40974.40081655905
 2021-03-16,56025.67583148412
 2021-03-17,42957.88421097572
 2021-03-18,65464.99283743926
 2021-03-19,28628.77720679815
 2021-03-20,50873.13077669
 2021-03-21,47215.115350042746
 2021-03-22,44982.481462385775
 2021-03-23,72845.44784612038
 2021-03-24,36657.28355561715
 2021-03-25,35932.62440127316
 2021-03-26,90802.53749884429
 2021-03-27,54150.36198995029
 2021-03-28,57725.715294590715
 2021-03-29,49797.541628930994
 2021-03-30,39842.574327318325
 2021-03-31,46648.05822011224
 2021-04-01,38251.20061495644
 2021-04-02,54968.95147105346
 2021-04-03,39223.33668121346
 2021-04-04,55196.72314245463
 2021-04-05,60193.96623402014
 2021-04-06,107790.97235982082
 2021-04-07,58927.3553815537
 2021-04-08,52570.524217849554
 2021-04-09,40996.74684261808
 2021-04-10,52952.91853803685
 2021-04-11,55117.279622249654
 2021-04-12,62330.90239991735
 2021-04-13,60352.159875666686
 2021-04-14,46481.1929993728
 2021-04-15,26740.04853400801
 2021-04-16,58824.75809726865
 2021-04-17,48919.848176294996
 2021-04-18,69164.97343682637
 2021-04-19,65052.99346838036
 2021-04-20,86948.6316872793
 2021-04-21,47600.922050548594
 2021-04-22,53629.43407349051
 2021-04-23,58802.85640700405
 2021-04-24,27277.291629712032
 2021-04-25,72991.08369503866
 2021-04-26,46319.17825995694
 2021-04-27,42473.64434623195
 2021-04-28,62986.327912551824
 2021-04-29,59770.868769586974
 2021-04-30,46967.110213491585
 2021-05-01,31283.252270527257
 2021-05-02,45270.96133039481
 2021-05-03,40200.06151139432
 2021-05-04,28769.438869243786
 2021-05-05,41597.284397045456
 2021-05-06,42053.59694349442
 2021-05-07,32056.90063878994
 2021-05-08,24126.23251230451
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 2021-06-10,-146080.38556497518
 2021-06-11,-197744.22441062317
 2021-06-12,-247206.8338239739
 2021-06-13,-19232.408561805147
 2021-06-14,-47054.57870629374
 2021-06-15,-82331.94894057448
 2021-06-16,56822.28522437127
 2021-06-17,-5380.359315638307
 2021-06-18,-64742.15657142063
 2021-06-19,-102359.69861953295
 2021-06-20,-187079.5771196356
 2021-06-21,-237233.0730349294
 2021-06-22,-60781.477353804774
 2021-06-23,-95743.53688423763
 2021-06-24,219265.19183468865
 2021-06-25,166018.31299095904
 2021-06-26,127324.35545632138
 2021-06-27,62794.68059832805
 2021-06-28,21668.2514308655
 2021-06-29,-50316.4801029578
 2021-06-30,-79204.52348730947
 2021-07-01,191577.08330515135
 2021-07-02,141760.78489785508
 2021-07-03,98948.39846553442
 2021-07-04,64038.65918802995
 2021-07-05,26447.1233413148
 2021-07-06,-13022.080250524916
 2021-07-07,285296.5291195527
 2021-07-08,257249.25334153447
 2021-07-09,589937.2502111658
 2021-07-10,547332.2642310483
 2021-07-11,489625.47496736515
 2021-07-12,451659.6340056894
 2021-07-13,408940.087223126
 2021-07-14,372398.90279541345
 2021-07-15,327219.6904202084
 2021-07-16,278112.5707611354
 2021-07-17,249564.69143054486
 2021-07-18,199849.93454908518
 2021-07-19,633081.1550744908
 2021-07-20,560950.8118202476
 2021-07-21,885190.5961887846
 2021-07-22,838076.0106605014
 2021-07-23,775211.1163124711
 2021-07-24,726945.8405482947
 2021-07-25,644089.006161145
 2021-07-26,941593.1369481836
 2021-07-27,910279.2186288635
 2021-07-28,858576.4584500947
 2021-07-29,1188692.7272715634
 2021-07-30,1145293.3945720182
 2021-07-31,1089868.8541963038
 2021-08-01,1047642.9074704085
 2021-08-02,1012835.3742754249
 2021-08-03,1238891.4321784496
 2021-08-04,1211681.9816677598
 2021-08-05,1154444.895439112
 2021-08-06,1109275.8181821972
 2021-08-07,1058247.3735601068
 2021-08-08,1017256.953908889
 2021-08-09,985824.1863912917
 2021-08-10,1275142.1161176958
 2021-08-11,1207694.6598353714
 2021-08-12,1151633.8969831534
 2021-08-13,1115537.8540568247
 2021-08-14,1033378.6927169448
 2021-08-15,971994.1544095458
 2021-08-16,929073.1323963873
 2021-08-17,871498.3232116804
 2021-08-18,1108483.0617190541
 2021-08-19,1070370.8727955446
 2021-08-20,1027068.5970765499
 2021-08-21,1361247.2714829298
 2021-08-22,1309791.1082427092
 2021-08-23,1258414.6965946767
 2021-08-24,1212790.1028415759
 2021-08-25,1166055.3208899843
 2021-08-26,1128490.2460538154
 2021-08-27,1104935.8483842565
 2021-08-28,1084330.9002424532
 2021-08-29,1017996.6412879428
 2021-08-30,969948.7871034581
 2021-08-31,913757.0731994106
 2021-09-01,1250598.2141536232
 2021-09-02,1204568.0666521925
 2021-09-03,1193864.2432135388
 2021-09-04,1123520.6427849764
 2021-09-05,1053913.50147074
 2021-09-06,996981.9493567909
 2021-09-07,942318.3358728208
 2021-09-08,884592.806864691
 2021-09-09,830835.4141095028
 2021-09-10,777354.6650491382
 2021-09-11,727752.3731808765
 2021-09-12,674766.4777472744
 2021-09-13,637354.7405956141
 2021-09-14,908897.880879431
 2021-09-15,886906.8587683073
 2021-09-16,827398.073433537
 2021-09-17,785853.9417255967
 2021-09-18,738975.7754809558
 2021-09-19,706505.9460452488
 2021-09-20,644699.676989112
 2021-09-21,588624.9513246977
 2021-09-22,535141.1458672826
 2021-09-23,505063.9366007591
 2021-09-24,431993.38810376957
 2021-09-25,387875.01040075195
 2021-09-26,353740.67433509044
 2021-09-27,292121.1635336504
 2021-09-28,262786.2040530068
 2021-09-29,207945.42564793542
 2021-09-30,153084.1661070135
 2021-10-01,115417.47204050998
 2021-10-02,69902.58229749798
 2021-10-03,32029.986340895797
 2021-10-04,257910.21700555782
 2021-10-05,185741.80633443507
 2021-10-06,127330.03843891155
 2021-10-07,62879.39650024672
 2021-10-08,23033.226504836108
 2021-10-09,-27979.69656548275
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 2021-10-11,196022.73780922336
 2021-10-12,137816.2770916728
 2021-10-13,91328.3314459105
 2021-10-14,41251.62959627359
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 2021-10-17,-99851.2698825248
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 2021-10-19,-212049.14390664152
 2021-10-20,-245454.1192965511
 2021-10-21,-305386.0794143668
 2021-10-22,-120943.28550890283
 2021-10-23,-171138.31388707145
 2021-10-24,-211211.51691554562
 2021-10-25,15210.595103964442
 2021-10-26,-46516.747972695215
 2021-10-27,-108704.63530860818
 2021-10-28,-162919.51332463368
 2021-10-29,-230061.855542359
 2021-10-30,-276549.55492150894
 2021-10-31,60181.492596672266
 2021-11-01,11902.539218675753
 2021-11-02,-8816.14378848672
 2021-11-03,-66954.54444227618
 2021-11-04,-116676.84740239033
 2021-11-05,-153418.98585937332
 2021-11-06,-196155.47476637954
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 2021-11-08,-333344.50629424525
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 2021-11-10,-43261.74829909939
 2021-11-11,-106048.24832104275
 2021-11-12,-143774.93807254065
 2021-11-13,-210808.42267524963
 2021-11-14,-266175.2330804739
 2021-11-15,-320073.4749942003
 2021-11-16,-382919.4569090524
 2021-11-17,-416753.2852401128
 2021-11-18,-477830.28394004394
 2021-11-19,-529058.3960308388
 2021-11-20,-584693.8663060239
 2021-11-21,-656496.8774633836
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 2021-11-23,-458715.2946404286
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 2021-11-25,-589663.322212392
 2021-11-26,-636461.6149367143
 2021-11-27,-672820.8031147933
 2021-11-28,-734332.3240520869
 2021-11-29,-773617.0527816914
 2021-11-30,-802079.1205139998
 2021-12-01,-525354.9727204265
 2021-12-02,-584780.1553593907
 2021-12-03,-627794.2090558368
 2021-12-04,-680563.7169338213
 2021-12-05,-358688.0285447965
 2021-12-06,-417863.17287740955
 2021-12-07,-94466.31801389478
 2021-12-08,-120354.06949547637
 2021-12-09,270468.6405604635
 2021-12-10,220782.1644699257
 2021-12-11,525267.2813442927
 2021-12-12,482176.8629086895
 2021-12-13,453361.41842871887
 2021-12-14,421915.6790918901
 2021-12-15,370951.4788054582
 2021-12-16,333261.71358073386
 2021-12-17,293991.26921963337
 2021-12-18,230223.3400138117
 2021-12-19,163977.57136618256
 2021-12-20,99667.54491378451
 2021-12-21,79481.0786327979
 2021-12-22,250930.1081393262
 2021-12-23,426844.782771413
 2021-12-24,366615.4882019885
 2021-12-25,308825.2904883709
 2021-12-26,248976.98635886348
 2021-12-27,200133.51200007505
 2021-12-28,154650.06743391437
 2021-12-29,109288.25307168259
 2021-12-30,52184.36661215486
 2021-12-31,2705.043157733511
--- a/archive/2122/cases/case_1/summary_first_sweep.csv
+++ b/archive/2122/cases/case_1/summary_first_sweep.csv
@ -1,10 +0,0 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,0.8,0.0684931506849315,0.0726027397260274,0.0222201370969798,10,-1594714.1742160032,-1530433.4046675265,285066.2951297739,10,50.2,50.0,1.3984117975602024,10
 0.8,1.0,0.5816438356164383,0.6520547945205479,0.25980427056171707,10,-20433.232547621035,66493.16878082216,231209.05072598366,10,49.4,49.5,0.9660917830792962,10
 0.8,1.2,0.9356164383561645,0.9534246575342465,0.040805672352052756,10,260408.40724531026,264591.4202889103,34451.38595202987,10,42.5,42.5,0.5270462766947288,10
 1.0,0.8,0.11863013698630136,0.10547945205479452,0.06135803865213693,10,-1360077.266559611,-1421026.0243578043,341686.4813035888,10,50.7,50.5,1.3374935098492577,10
 1.0,1.0,0.6550684931506849,0.7821917808219179,0.2787601486044031,10,63898.06455496148,158892.80360797068,215553.80705682412,10,49.3,49.5,1.1595018087284057,10
 1.0,1.2,0.9904109589041095,0.9945205479452055,0.011854570763239407,10,360626.1147252074,359297.0984386411,24074.260208145475,10,42.1,42.0,0.5676462121975469,10
 1.2,0.8,0.14684931506849314,0.11095890410958904,0.1127785478486988,10,-1471541.3243492458,-1590944.9289921233,402072.5562878826,10,50.1,50.5,1.7919573407620821,10
 1.2,1.0,0.8868493150684932,0.963013698630137,0.15021679044253444,10,289885.33637142787,340960.57915301056,116064.12202102688,10,49.9,50.0,0.7378647873726214,10
 1.2,1.2,0.996986301369863,1.0,0.007126799274932135,10,464072.69404238986,464252.8407907331,35729.24312964791,10,42.0,42.0,0.6666666666666669,10
--- a/archive/2122/cases/case_1/summary_second_sweep.csv
+++ b/archive/2122/cases/case_1/summary_second_sweep.csv
@ -1,26 +0,0 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.0,0.5629041095890411,0.5630136986301371,0.25173917949278124,100,-17208.65752949769,31032.43630715287,200730.55535889775,100,49.05,49.0,1.166666666666668,100
 0.8,1.05,0.804986301369863,0.8452054794520547,0.14385248604678155,100,155614.19545368513,176960.00812669177,89250.77498164216,100,47.39,47.0,0.7900262110470866,100
 0.8,1.1,0.8659726027397261,0.8876712328767123,0.09160908657556802,100,198680.3804320387,207071.20423920936,54616.27896318188,100,45.45,45.0,0.7017294652672376,100
 0.8,1.15,0.924082191780822,0.9342465753424658,0.042773373946112477,100,240782.04918482568,243459.64652624974,29223.097332241305,100,43.46,43.5,0.7305733185227711,100
 0.8,1.2,0.9414794520547946,0.9493150684931506,0.03451593530365504,100,261676.29869938738,264285.581551831,25131.022316170343,100,41.83,42.0,0.6971080231639825,100
 0.9,1.0,0.6648493150684932,0.6945205479452055,0.21570939170830844,100,73024.72198996953,104935.78104756039,163647.97407003478,100,49.32,49.0,0.8862587350511948,100
 0.9,1.05,0.8186575342465754,0.8753424657534246,0.16907143140798406,100,182114.7427397742,214236.05435841338,108978.10420990313,100,47.46,47.0,0.9147500624584167,100
 0.9,1.1,0.9226849315068493,0.9397260273972603,0.06860053019119008,100,257549.83941332222,264802.2368256551,46594.05059506942,100,45.65,46.0,0.7833494518006421,100
 0.9,1.15,0.9611506849315069,0.9726027397260274,0.040083238669095175,100,297073.0555063267,302993.9769790715,33827.59431750586,100,43.63,44.0,0.7057484028184599,100
 0.9,1.2,0.9742739726027397,0.9808219178082191,0.020594090521446923,100,316243.2068669456,319849.0246099904,23997.41545045925,100,42.05,42.0,0.6871842709362761,100
 1.0,1.0,0.6921643835616439,0.7698630136986302,0.2448481301495943,100,101882.22346645949,160026.78900577017,180014.0847194441,100,49.41,49.0,1.0739806066379143,100
 1.0,1.05,0.8867945205479453,0.936986301369863,0.1266759950750295,100,246218.68552190147,273469.8906422792,85390.22913704212,100,47.72,48.0,0.7923880286064316,100
 1.0,1.1,0.9432054794520548,0.9698630136986301,0.07141874718689835,100,301924.2700638766,316299.9013534653,59963.75704883238,100,45.76,46.0,0.7123726184201268,100
 1.0,1.15,0.9764109589041096,0.9863013698630136,0.02872101571594946,100,340290.7186400273,343600.27244723897,34849.39399657999,100,43.88,44.0,0.7286350876186868,100
 1.0,1.2,0.9906575342465753,0.9945205479452055,0.0113001726894409,100,369036.41037286026,371895.01996879186,24001.498220542777,100,42.05,42.0,0.6256309946079575,100
 1.1,1.0,0.7282465753424657,0.7479452054794521,0.2260275046018253,100,139083.56089769275,162758.51029021217,168922.45751711642,100,49.45,49.5,1.0576799462440758,100
 1.1,1.05,0.9035068493150685,0.9671232876712329,0.16213843415295967,100,289746.11535166804,320941.33405487105,114866.00516221055,100,47.71,48.0,0.7951240294584386,100
 1.1,1.1,0.9613698630136986,0.989041095890411,0.0655565291501118,100,350031.4173621522,367119.16888596804,60281.0758667702,100,45.9,46.0,0.7719841941125448,100
 1.1,1.15,0.9912054794520548,0.9972602739726028,0.017645347308402025,100,396603.9442134802,400346.3145094304,29483.57515994371,100,44.0,44.0,0.7247430753394793,100
 1.1,1.2,0.9956164383561643,1.0,0.007052473418545561,100,417138.91165137023,418615.9689806167,23055.605401908946,100,42.1,42.0,0.6741998624632427,100
 1.2,1.0,0.7846575342465754,0.8698630136986301,0.2252867428579435,100,193695.8595228242,242047.6119066357,194075.09374543617,100,49.79,50.0,1.121822110891404,100
 1.2,1.05,0.9479178082191781,0.9780821917808219,0.09026491930718351,100,346443.9102815047,358689.5454693798,87392.79621129965,100,48.05,48.0,0.7436600722307887,100
 1.2,1.1,0.9872602739726026,0.9972602739726028,0.025089991771959474,100,411297.4581000472,416474.64406344807,44380.80426082012,100,46.0,46.0,0.7106690545187017,100
 1.2,1.15,0.9916438356164383,1.0,0.022282747753160106,100,441406.6727818038,448100.49807167007,38234.435371301166,100,44.19,44.0,0.7063206700139026,100
 1.2,1.2,0.9969589041095891,1.0,0.007179214412726819,100,468382.008571895,474174.00471737224,28487.211871335898,100,42.35,42.0,0.6723244767373897,100
--- a/archive/2122/cases/case_1/summary_third_sweep.csv
+++ b/archive/2122/cases/case_1/summary_third_sweep.csv
@ -1,71 +0,0 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.05,0.7859726027397261,0.8260273972602741,0.1598362225708639,100,144208.47823315178,169318.30356595665,100514.74254469664,100,47.4,47.0,0.8164965809277261,100
 0.8,1.06,0.794958904109589,0.8301369863013699,0.1403919914831539,100,153586.2551041631,173418.410864591,78290.88655784576,100,47.11,47.0,0.7371114795831993,100
 0.8,1.07,0.8067671232876712,0.8315068493150686,0.1339955549137507,100,161441.590865957,172098.93199610294,72690.59543201819,100,46.56,47.0,0.7291893935852132,100
 0.8,1.08,0.8381917808219178,0.8493150684931507,0.10243407881742274,100,180757.48462696798,185912.87023764107,63117.92391789514,100,46.47,46.0,0.7843803447642761,100
 0.8,1.09,0.8667671232876712,0.8808219178082193,0.08505385831720369,100,198896.08671044532,205127.32601251808,47198.21828806646,100,45.77,46.0,0.664466064983872,100
 0.8,1.1,0.878027397260274,0.9054794520547945,0.09679462977911307,100,207399.79024280803,225251.01385664742,55542.82909581238,100,45.52,46.0,0.7032392583932264,100
 0.8,1.11,0.8961643835616439,0.9123287671232877,0.06811908370060707,100,218048.13332393923,226635.2932288471,41083.817034265245,100,45.0,45.0,0.7654139963827331,100
 0.8,1.12,0.9054246575342466,0.9178082191780822,0.05473703917338135,100,226233.79870989014,228435.7464824344,33790.75003378518,100,44.75,45.0,0.770346898114313,100
 0.8,1.13,0.9113972602739726,0.9219178082191781,0.057722017942900286,100,229820.05176361586,234205.64538325084,37180.29105911812,100,44.41,44.0,0.6371495868001452,100
 0.8,1.14,0.9096164383561645,0.9260273972602739,0.06476061395141362,100,229451.0061609741,236898.16887785456,39996.703715484575,100,43.98,44.0,0.6192207550093444,100
 0.9,1.05,0.799068493150685,0.8589041095890411,0.17478733730560359,100,170856.8688515813,195706.0585029618,107559.81482477198,100,47.66,48.0,0.7278028371042323,100
 0.9,1.06,0.8728493150684932,0.9178082191780822,0.14035816656132336,100,214583.50564701803,237766.20853748685,91537.75933622228,100,47.16,47.0,0.9180567971690441,100
 0.9,1.07,0.8863835616438357,0.9246575342465753,0.13202268131162476,100,225962.8131575521,248956.4255873827,87091.59551249178,100,46.85,47.0,0.7703468981143126,100
 0.9,1.08,0.8878356164383561,0.9109589041095891,0.1034915126220675,100,231793.74733914566,241230.59100630687,65364.238707012075,100,46.37,46.0,0.7608474807008903,100
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 0.95,1.11,0.9364383561643835,0.9561643835616438,0.06526268605058627,100,280881.6829739677,296677.6782650106,49506.88718738129,100,45.34,45.0,0.6391281940932124,100
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 0.95,1.13,0.9625479452054795,0.9753424657534246,0.03853770121887277,100,303624.8432092603,305964.4471615079,33997.45186113061,100,44.56,45.0,0.6562827673397108,100
 0.95,1.14,0.965041095890411,0.9808219178082191,0.04503533313503058,100,309887.30047959444,316649.4679522059,37629.163123648126,100,44.27,44.0,0.6942039987778751,100
 1.0,1.05,0.878054794520548,0.9232876712328767,0.12456329754626162,100,239357.93264631205,263694.2998236382,80089.22781996857,100,47.79,48.0,0.8680059581790576,100
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 1.0,1.08,0.9243287671232876,0.952054794520548,0.08231970120182112,100,281449.77736003743,293460.12722831377,61529.54372494277,100,46.59,47.0,0.726065917924618,100
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 1.0,1.1,0.9562739726027396,0.9753424657534246,0.05972417397215357,100,312685.41726779164,317915.94000983867,49197.89880376956,100,45.67,46.0,0.7792122414154682,100
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 1.0,1.12,0.9685479452054794,0.9835616438356164,0.04226908937517442,100,325205.9243724567,337201.9597184559,45250.733446382925,100,44.9,45.0,0.6890192121758836,100
 1.0,1.13,0.9677808219178082,0.9808219178082191,0.04733685887394322,100,330657.6603956923,337075.92965660174,42421.01611114322,100,44.6,45.0,0.7247430753394793,100
 1.0,1.14,0.9823013698630138,0.9917808219178083,0.02489902195487209,100,346636.5895811218,351643.50356452586,32724.192897340898,100,44.11,44.0,0.6947857747012907,100
 1.05,1.05,0.9096712328767123,0.9506849315068493,0.1268551724970295,100,275036.8994307617,290241.47330518905,92189.74314580947,100,47.76,48.0,0.8542230210530327,100
 1.05,1.06,0.9146575342465754,0.9561643835616438,0.12009700618062043,100,283383.00552277366,305048.07351176,96246.65543086598,100,47.27,47.0,0.7365631354644712,100
 1.05,1.07,0.9413972602739726,0.9698630136986301,0.09003060152571733,100,303722.93403703946,313331.6029302139,73057.08547382594,100,46.91,47.0,0.7533762390570582,100
 1.05,1.08,0.9469315068493152,0.9780821917808219,0.0793003279699756,100,317055.4136440441,329838.54958045797,63485.26929871185,100,46.59,47.0,0.7666666666666667,100
 1.05,1.09,0.9626575342465753,0.9835616438356164,0.050483002706923574,100,327783.50728198123,337811.29494201415,48868.03042293043,100,46.26,46.0,0.7603826787755086,100
 1.05,1.1,0.9624109589041097,0.9794520547945205,0.05435562429151819,100,331620.04862504784,339715.6067690905,51905.17082289278,100,45.78,46.0,0.7464393593398702,100
 1.05,1.11,0.9697534246575343,0.989041095890411,0.04971307496436453,100,340227.99533744523,351120.098823712,49956.8593361718,100,45.43,45.0,0.7420283421851632,100
 1.05,1.12,0.9702739726027397,0.9863013698630136,0.05158788657894017,100,343659.58516506077,353342.32752993377,44689.97958762292,100,45.05,45.0,0.7299508769967222,100
 1.05,1.13,0.9841643835616438,0.9917808219178083,0.021220325968317567,100,362923.71636342397,367782.8510066926,30511.492738523157,100,44.68,45.0,0.7089613971340485,100
 1.05,1.14,0.9803287671232878,0.989041095890411,0.03439019008566572,100,358818.66979266173,366030.73615185026,38779.97603060032,100,44.42,44.0,0.6693883835866954,100
 1.1,1.05,0.8774520547945205,0.9493150684931506,0.16332896109997266,100,262116.4533938322,307218.83635365096,137001.0438185493,100,47.65,48.0,0.845367650579594,100
 1.1,1.06,0.93,0.9575342465753425,0.08998299067660721,100,308184.9288191942,315262.4431269691,76531.77678714157,100,47.33,47.0,0.7114503609961922,100
 1.1,1.07,0.9332054794520548,0.9767123287671233,0.1133241844881452,100,316339.8582983121,343388.79801796563,88723.83399772066,100,47.18,47.0,0.7704124567628391,100
 1.1,1.08,0.9466849315068494,0.9767123287671233,0.09839032697603153,100,331184.26416084,349136.8427766626,81927.62325661813,100,46.52,47.0,0.8346050901867643,100
 1.1,1.09,0.9556164383561644,0.989041095890411,0.10547415073320952,100,344293.7967364825,363724.13265618257,85256.01192642892,100,46.26,46.0,0.7333333333333325,100
 1.1,1.1,0.9733150684931507,0.9945205479452055,0.06351837655542099,100,363147.94176298834,374326.82103307196,57145.65533432398,100,45.75,46.0,0.6256309946079563,100
 1.1,1.11,0.9746849315068493,0.9917808219178083,0.04402911492630584,100,363388.1927891669,373491.37867364136,49274.54501585131,100,45.5,45.0,0.6435381994422805,100
 1.1,1.12,0.9793972602739726,0.9917808219178083,0.03915141511936552,100,370890.05170730536,376338.38252097333,44302.65600471223,100,45.1,45.0,0.7035264706814484,100
 1.1,1.13,0.9804109589041096,0.9945205479452055,0.03243412017637617,100,377327.82974791917,381348.09184570936,38488.17279855358,100,44.76,45.0,0.7123726184201263,100
 1.1,1.14,0.9886849315068492,0.9945205479452055,0.017334789000443024,100,384809.10717216856,390844.7511152257,31093.991487524258,100,44.51,44.0,0.6589707309451781,100
 1.2,1.05,0.9171780821917808,0.9726027397260274,0.13067274741673798,100,322060.45665888296,340538.9200925699,105710.6766863121,100,47.92,48.0,0.8490041700769674,100
 1.2,1.06,0.9489041095890411,0.9917808219178083,0.09929085079019878,100,355803.9539068448,384192.0333909353,97019.18401447838,100,47.61,48.0,0.7771353768420244,100
 1.2,1.07,0.9564109589041097,0.9917808219178083,0.07656699649287621,100,364057.84548111144,389141.36283888033,75902.53849836365,100,47.31,47.0,0.6918720031095832,100
 1.2,1.08,0.9602465753424657,0.989041095890411,0.08505995544913891,100,373446.1741382272,386689.89061583555,83449.85958080231,100,46.81,47.0,0.6918720031095821,100
 1.2,1.09,0.9672054794520548,0.9945205479452055,0.08413140089715565,100,390837.72881569655,407689.56891719927,79174.94776761712,100,46.26,46.0,0.7194273817250479,100
 1.2,1.1,0.9738356164383561,0.9972602739726028,0.060117001280751294,100,398871.1618030684,414081.79698469874,60975.215674990715,100,46.23,46.0,0.6794977348874467,100
 1.2,1.11,0.9765205479452055,0.9972602739726028,0.05166994864627592,100,404329.81311479496,423147.71118130337,66661.79450110722,100,45.64,46.0,0.6744994402884964,100
 1.2,1.12,0.9849315068493151,0.9972602739726028,0.04728618617716756,100,424054.9128070699,431696.0971162426,53638.952963032265,100,45.25,45.0,0.7436600722307911,100
 1.2,1.13,0.991041095890411,0.9986301369863013,0.018392476408138413,100,428392.03453004366,434494.4380003613,39705.52165661862,100,44.91,45.0,0.6528105515090998,100
 1.2,1.14,0.992986301369863,1.0,0.01664151932365369,100,438627.18168542744,445798.6518808934,35926.80443305188,100,44.45,44.0,0.6871842709362769,100
--- a/archive/2122/cases/case_2/case_2_description.docx
+++ b/archive/2122/cases/case_2/case_2_description.docx
--- a/archive/2122/cases/case_2/case_2_description.pdf
+++ b/archive/2122/cases/case_2/case_2_description.pdf
--- a/archive/2122/cases/case_2/case_2_student_notebook.ipynb
+++ b/archive/2122/cases/case_2/case_2_student_notebook.ipynb
@ -1,601 +0,0 @@
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "case_2_student_notebook.ipynb",
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to PuLP\n",
        "\n",
        "For case 2, you will need to define and solve optimization problems. In this notebook, I'll help you understand how to use `pulp`, a Python package for modeling optimization problems. You might want to check the following links:\n",
        "\n",
        "- Documentation: https://coin-or.github.io/pulp/\n",
        "- Homepage: https://github.com/coin-or/pulp\n",
        "\n"
      ],
      "metadata": {
        "id": "eLvjUuJdzS7z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing and checking all is in place"
      ],
      "metadata": {
        "id": "HFavOEVS0dbY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first thing you need to do is to install `pulp`. `pulp` is not in the standard available packages in Colab, so you need to run the following cell once. "
      ],
      "metadata": {
        "id": "HgZwpjUG0PsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pulp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ni6Q_YiO0nIm",
        "outputId": "405d3f57-4502-4ed5-dcb9-d3204b585bb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pulp\n",
            "  Downloading PuLP-2.6.0-py3-none-any.whl (14.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.2 MB 8.9 MB/s \n",
            "\u001b[?25hInstalling collected packages: pulp\n",
            "Successfully installed pulp-2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After doing that, you can import the library."
      ],
      "metadata": {
        "id": "k9YI0Kzw0qLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pulp"
      ],
      "metadata": {
        "id": "hw6keX7x0tZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If all is good, running the following command will print a large log testing `pulp`. The last line should read \"OK\"."
      ],
      "metadata": {
        "id": "vD_rXehL1KXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pulp.pulpTestAll()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ney2a8mu1JqQ",
        "outputId": "a6b32d96-b163-4fc5-b4fc-3d5673e5a40a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss.........."
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Test that logic put in place for deprecation handling of indexs works\n",
            "\t Testing 'indexs' param continues to work for LpVariable.dicts\n",
            "\t Testing 'indexs' param continues to work for LpVariable.matrix\n",
            "\t Testing 'indices' argument works in LpVariable.dicts\n",
            "\t Testing 'indices' param continues to work for LpVariable.matrix\n",
            "\t Testing invalid status\n",
            "\t Testing continuous LP solution - export dict\n",
            "\t Testing export dict for LP\n",
            "\t Testing export dict MIP\n",
            "\t Testing maximize continuous LP solution\n",
            "\t Testing continuous LP solution - export JSON\n",
            "\t Testing continuous LP solution - export solver dict\n",
            "\t Testing continuous LP solution - export solver JSON\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            ".........."
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Testing reading MPS files - binary variable, no constraint names\n",
            "\t Testing reading MPS files - integer variable\n",
            "\t Testing reading MPS files - maximize\n",
            "\t Testing invalid var names\n",
            "\t Testing logPath argument\n",
            "\t Testing makeDict general behavior\n",
            "\t Testing makeDict default value behavior\n",
            "\t Testing measuring optimization time\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "............."
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Testing the availability of the function pulpTestAll\n",
            "\t Testing zero subtraction\n",
            "\t Testing inconsistent lp solution\n",
            "\t Testing continuous LP solution\n",
            "\t Testing maximize continuous LP solution\n",
            "\t Testing unbounded continuous LP solution\n",
            "\t Testing Long Names\n",
            "\t Testing repeated Names\n",
            "\t Testing zero constraint\n",
            "\t Testing zero objective\n",
            "\t Testing LpVariable (not LpAffineExpression) objective\n",
            "\t Testing Long lines in LP\n",
            "\t Testing LpAffineExpression divide\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "............."
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Testing MIP solution\n",
            "\t Testing MIP solution with floats in objective\n",
            "\t Testing Initial value in MIP solution\n",
            "\t Testing fixing value in MIP solution\n",
            "\t Testing MIP relaxation\n",
            "\t Testing feasibility problem (no objective)\n",
            "\t Testing an infeasible problem\n",
            "\t Testing an integer infeasible problem\n",
            "\t Testing another integer infeasible problem\n",
            "\t Testing column based modelling\n",
            "\t Testing dual variables and slacks reporting\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "..........ssssssssssssssssssssssssssssssssssssssssssssssssssssss"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Testing fractional constraints\n",
            "\t Testing elastic constraints (no change)\n",
            "\t Testing elastic constraints (freebound)\n",
            "\t Testing elastic constraints (penalty unchanged)\n",
            "\t Testing elastic constraints (penalty unbounded)\n",
            "\t Testing timeLimit argument\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss\n",
            "----------------------------------------------------------------------\n",
            "Ran 840 tests in 15.681s\n",
            "\n",
            "OK (skipped=784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining and solving problems\n",
        "\n",
        "The following cells show you the absolute minimum to model and solve a problem with `pulp`. The steps are:\n",
        "\n",
        "1. Define decision variables\n",
        "2. Define the target function\n",
        "3. Define the constraints\n",
        "4. Assemble the problem\n",
        "5. Solve it\n",
        "6. Examine results\n",
        "\n",
        "For more flexibility, options and interesting stuff, please check up the PuLP documentation."
      ],
      "metadata": {
        "id": "oiXz40NR1whf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define decision variables"
      ],
      "metadata": {
        "id": "nq5bcQs03g0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = pulp.LpVariable(\n",
        "    name=\"x\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "y = pulp.LpVariable(\n",
        "    name=\"y\",\n",
        "    cat=pulp.LpInteger # This will make the variable integer only\n",
        "    )\n",
        "\n",
        "z = pulp.LpVariable(\n",
        "    name=\"z\",\n",
        "    cat=pulp.LpBinary # This will make the variable binary (only 0 or 1)\n",
        ")"
      ],
      "metadata": {
        "id": "0SPhww4L3buh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define the target function"
      ],
      "metadata": {
        "id": "uhlbq2oO35kp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 10 * x - 5 * y + z"
      ],
      "metadata": {
        "id": "pu3Im9DH39CN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define constraints"
      ],
      "metadata": {
        "id": "lqD0dD474Izw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "constraint_1 = x >= 0\n",
        "constraint_2 = y >= 0\n",
        "constraint_3 = x >= 10\n",
        "constraint_4 = y <= 50"
      ],
      "metadata": {
        "id": "5Cu51lYj4OUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assemble the problem\n",
        "\n",
        "To put all the parts together, you need to declare a problem and specify if you want to minimize or maximize the target function.\n",
        "\n",
        "Once you have that:\n",
        "- First, you \"add\" the target function.\n",
        "- After, you \"add\" all the constraints you want to include."
      ],
      "metadata": {
        "id": "d5nq94IM4kSU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"my_silly_problem\", pulp.LpMinimize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    constraint_1,\n",
        "    constraint_2,\n",
        "    constraint_3,\n",
        "    constraint_4\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "yI-Oiwh64mRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Solve it\n",
        "\n",
        "The problem object is now unsolved. You can call the `solve` method on it to find a solution."
      ],
      "metadata": {
        "id": "RJTWfR8-5fBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Fbltpbp5mRi",
        "outputId": "07f9c959-e9b0-4fe7-e7ea-c698703111ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine results\n",
        "\n",
        "After calling `solve` on a problem, you can access:\n",
        "- The status of the problem. It can be solved, but also it might show to be not feasible.\n",
        "- The values assigned to each decision variable.\n",
        "- The final value for the target function.\n",
        "\n"
      ],
      "metadata": {
        "id": "0pc9RmrO7FKo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(pulp.value(problem.objective))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8U4xVvUg9W07",
        "outputId": "32a330f1-65ab-4903-f29b-368f2bacaf94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Status: Optimal\n",
            "x = 10.0\n",
            "y = 50.0\n",
            "z = 0.0\n",
            "-150.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Peanut Butter Example\n",
        "\n",
        "As an additional example, you can find below the model and solver for the Peanut Butter Sandwich example we discussed on lecture 6."
      ],
      "metadata": {
        "id": "I2lNaFm2XVK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pb = pulp.LpVariable(\n",
        "    name=\"Peanut Butter grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "b = pulp.LpVariable(\n",
        "    name=\"Bread grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )"
      ],
      "metadata": {
        "id": "HI4E2dNoXVK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 5.88 * pb + 2.87 * b"
      ],
      "metadata": {
        "id": "PfTxq8R0XVLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_negative_pb = pb >= 0\n",
        "no_negative_b = b >= 0\n",
        "max_pb_we_have = pb <= 200\n",
        "max_b_we_have = b <= 300\n",
        "doctors_dietary_restriction = pb <= 0.13 * b"
      ],
      "metadata": {
        "id": "2X1AzQM8XVLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"sandwich_problem\", pulp.LpMaximize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    no_negative_pb,\n",
        "    no_negative_b,\n",
        "    max_pb_we_have,\n",
        "    max_b_we_have,\n",
        "    doctors_dietary_restriction\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "3oEoQXebXVLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()\n",
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(f\"Final calories: {pulp.value(problem.objective)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d873d58d-6d9e-459e-d66f-127f436b1aab",
        "id": "u1vI73kiXVLF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Status: Optimal\n",
            "Bread_grams = 300.0\n",
            "Peanut_Butter_grams = 39.0\n",
            "1090.32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case 2\n",
        "\n",
        "You can use the rest of the notebook to work on the different parts of case 2."
      ],
      "metadata": {
        "id": "6kWgbTjU-LaN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Good luck!"
      ],
      "metadata": {
        "id": "aYzseTWh-Sal"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
 }
--- a/archive/2122/cases/case_2/grading.xlsx
+++ b/archive/2122/cases/case_2/grading.xlsx
--- a/archive/2122/cases/case_2/grading_team_15.xlsx
+++ b/archive/2122/cases/case_2/grading_team_15.xlsx
--- a/archive/2122/cases/case_2/grading_team_4.xlsx
+++ b/archive/2122/cases/case_2/grading_team_4.xlsx
--- a/archive/2122/cases/case_2/grading_team_6.xlsx
+++ b/archive/2122/cases/case_2/grading_team_6.xlsx
--- a/archive/2122/cases/case_2/grading_team_7.xlsx
+++ b/archive/2122/cases/case_2/grading_team_7.xlsx
--- a/archive/2122/cases/case_2/grading_team_8.xlsx
+++ b/archive/2122/cases/case_2/grading_team_8.xlsx
--- a/archive/2122/cases/case_2/grading_team_9.xlsx
+++ b/archive/2122/cases/case_2/grading_team_9.xlsx
--- a/archive/2122/cases/case_2/prices_and_capacities.csv
+++ b/archive/2122/cases/case_2/prices_and_capacities.csv
@ -1,9 +0,0 @@
 dock,40_ft_container_price_eur,max_capacity
 Rotterdam,470,33000
 Antwerp,470,25000
 Hamburg,480,44000
 Amsterdam,610,11000
 Marseille,380,9000
 Algeciras,280,20000
 Valencia,310,11000
 Genoa,340,7500
--- a/archive/2122/cases/case_3/case_3_description.docx
+++ b/archive/2122/cases/case_3/case_3_description.docx
--- a/archive/2122/cases/case_3/case_3_description.md
+++ b/archive/2122/cases/case_3/case_3_description.md
@ -1,185 +0,0 @@
 # Case 3: Improving last mile logistics with Machine Learning
 After your last engagement with Charles, you have pretty much become the go-to
 service provider for advanced quantitative methods. Congratulations!
 You have been called again to help a different manager within Beanie Limited:
 Estefania Pelaez. Estefania is the city manager for Barcelona. She is in charge
 of all commercial and logistic operations that happen in the city.
 One of the operations that Beanie Limited runs in Barcelona is their own
 last-mile coffee delivery service. The company runs a small fleet of vans and
 trucks that delivers small quantities of roasted coffee beans (typically,
 around 10-100kg of coffee per delivery) to restaurants, cafes, hotels and other
 businesses in the city.
 The efficiency of the deliveries is important to keep margins profitable for
 Beanie Limited. A sloppy management can make the company lose money. Hence,
 Estefania is always working on ways to make the operations as smooth as
 possible.
 Currently, Beanie Limited has rented space in two warehouses: one located in
 Zona Franca and another one in Baro de Viver. Complementing that, the company
 has a small fleet of combi vans, regular sized vans and one truck, which are
 used by Beanie Limited own drivers to deliver the coffee beans from the
 warehouses to the customer's facilities.
 Orders placed by the customers are predictable and placed with time in advance,
 which allows Estefania and her team to plan the deliveries to minimize wasted
 effort by the fleet. Since they know which locations they will need to deliver
 to, they use a routing software that drafts the routes that each vehicle will
 cover each day.
 Recently, Estefania recently realized something: deliveries are almost always
 taking place too early or too late. After researching with some data, Estefania
 found out that there was nothing wrong with the routing software time
 estimates: the driving time between locations predicted by the software is
 accurate. The real issue is related to what Estefania's team calls the "
 engine-off" time.
 The engine-off time is the time a driver spends actually dropping off goods in
 a client location. It's called engine-off because the clock starts ticking when
 the driver takes the keys off the van and stops when the driver starts driving
 again.
 Currently, Estefania and her team assume an engine-off time of 3 minutes for
 all deliveries when building the delivery routes and schedules. But it seems
 that this not realistic at all and is causing a lot of trouble with the
 schedules. Clients are not happy with delivery times not being respected, some
 driver routes end up too early (which means that the same driver could have
 covered more clients) and some others run for too long (which means they have
 to go back to the warehouse without delivering all the goods requested by the
 clients).
 If Estefania could know beforehand what would be the engine-off time of
 different deliveries, she could improve the route planning to fix all of these
 issues. She has been told that Machine Learning could help with this and is
 expecting you to find out if and how it can be applied to this problem.
 ## Detailed Task Definition
 - Below you will find four levels of questions. Levels 1 to 3 are compulsory.
  Level 4 is optional.
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Estefania, should
  give him clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report and code.
 - You need to use a Python notebook to solve all levels. Please attach a
  notebook that shows your solution/proposal/analysis. Your notebook should be
  runnable "as-is". That means that anyone should be able to run it from
  beginning to end without any additional instructions or action required (
  except for uploading data from a CSV in the Google Colab environment. That
  requires someone to upload the file with a few clicksand it's fine).
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 By joining the customer database together with past deliveries details,
 Estefania has built a dataset of execute deliveries. The table contains 9,000
 examples of past deliveries and their engine-off times. The exact field
 meanings are explained below:
 - client_name: the name of the client.
 - truck_size: what type of truck was being used. Can be one of Combi, Van or
  Truck.
 - truck_origin_warehouse: from which Beanie Limited warehouse did the route
  start.
 - delivery_timestamp: at what date and time was the delivery done (defined as
  the moment the engine-off time starts).
 - total_weight: total weight of the goods delivery.
 - brand_1_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #1.
 - brand_2_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #2.
 - brand_3_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #3.
 - driver_id: the ID of the driver that was driving the route.
 - is_fresh_client: whether the client was fresh at the date of the delivery.
  Fresh clients are clients that have been doing business with Beanie for less
  than 30 days.
 - postcode: the postcode of the client location.
 - business_category: whether the client is a hotel, a cafe or restaurant or a
  coffee retailer.
 - floor: the physical position of the client location.
 - partnership_level: indicates the partnership level with Beanie. Key Account
  are important clients for Beanie Limited. Diamond clients are the top
  priority clients for the company.
 - box_count: how many distinct boxes were delivered to the client. The coffee
  beans bags are grouped into boxes for delivery.
 - final_time: the engine-off time, measured in seconds.
 ## Notebook
 Case 3 comes with no helping notebook: this time, you will have to code things
 from scratch yourselves. Remember that you are still suposed to write and
 deliver a notebook (see the "Detailed Task Definition" section).
 A few comments on your notebook:
 - I'm a going to constraint you to
  use [scikit-learn](https://scikit-learn.org/stable/) as a ML library. You can
  of course use other useful Python libraries such as pandas, numpy, etc. But
  for ML modeling, please go with scikit-learn.
 - Below you can find some useful materials which relate to what you need to do
  as part of the case:
    - [A simple, guided EDA on the Titanic Dataset](https://www.datacamp.com/tutorial/kaggle-machine-learning-eda)
    - [A guide on regression performance metrics](https://machinelearningmastery.com/regression-metrics-for-machine-learning/)
      and
      some [material from scikit-learn on the same topic](https://scikit-learn.org/stable/modules/classes.html#regression-metrics)
    - An [introduction to cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/)
    - A
      thorough [review on why we need to use baselines](https://blog.ml.cmu.edu/2020/08/31/3-baselines/)
      in ML
    - A
      simple [introduction to linear regression with scikit-learn](https://stackabuse.com/linear-regression-in-python-with-scikit-learn/)
      .
 ## Levels
 ### Level 1
 - Assess for Estefania if ML is a good choice for her problem and explain why.
 - Perform Exploratory Data Analysis on the given data. Is it clean? Which
  variables could be useful to explain the engine-off time? Are there any other
  interesting things you can draw from the dataset?
 ### Level 2
 - Present how are you going to measure performance for this problem and how you
  will use the available data for testing it.
 - Develop a baseline algorithm and evaluate its performance.
 ### Level 3
 - Develop the best model you can make to predict engine-off time.
 - Explain your methodology and report on performance.
 - Compare your performance to the baseline algorithm. Reflect on what is the
  cause of whatever differences can be observed between both.
 ### Level 4
 After presenting your model and results, Estefania has two different questions:
 - Estefania would like to learn from the ML algorithm. What are the most
  relevant features that define the engine-off time? Can you somehow quantify
  how important each is or which are most useful?
 - Estefania is interested in learning about next steps. What can be done to
  improve even more the model performance and achieve better results?
 ### SPECIAL
 For this case, we are going to run a little competition. There will be a
 surprise gift on the last lecture for the team that wins.
 The competition consists on getting the best performant model of the course. I
 have a hidden part of Estefania's dataset. If you present a notebook with a
 working model before the date XXXX (note this is earlier than the case delivery
 time), I will use your model to predict engine-off times on the hidden data.
 The team with the lowest error will win.
 To enter the competition, write a function at the end of your notebook
 called `predict_to_compete`. The function should take as its only input a
 dataframe with the same format as the shared dataset. The function should
 return a numpy array with the predicted drop-off times.
--- a/Show more
+++ b/Show more