📔
intro-to-python
  • An Introduction to Programming in Python (for Business Students)
  • exercises
    • Data Flow Diagramming Exercise
    • Developer Collaboration Exercise
    • README
    • "Web App" Exercise
      • checkpoints
        • Checkpoint 5: Bootstrap Layout
        • Checkpoint 4: Submitting Data from Web Forms
        • Checkpoint 3: Rendering HTML Pages
        • Checkpoint 1: Routing
        • Checkpoint 2: Modular Organization
      • "Web App" Exercise - Further Exploration
    • hello-world
      • "Hello World (Local)" Exercise
      • "Hello World (Local w/ Version Control)" Exercise
      • "Hello World (Colab)" Exercise
    • "Interface Capabilities" Exercise
    • "Continuous Integration 1, 2, 3" Exercise
    • "Web Service" Exercise
      • "Web Service" Exercise - Further Exploration
    • "Testing 1, 2, 3" Exercise
    • "Command-line Computing" Exercise
      • "Command-line Computing" Exercise
      • Professor Rossetti's Mac Terminal Configuration
      • Command-line Computing Exercise
    • "Codebase Cleanup" Assignment
    • "List Comprehensions" Exercise
    • "Groceries" Exercise
      • Python Datatypes (a.k.a. "Groceries") Exercise
      • Python Datatypes (a.k.a. "Groceries") Exercise
    • "Rock, Paper, Scissors" Exercise
      • "Rock, Paper, Scissors" Exercise
    • README
    • "Monthly Sales Predictions" Exercise
    • Setting up your Local Development Environment
    • "Chart Gallery" Exercise
    • "Run the App" Exercise
    • "Web Requests" Exercise
    • "API Client" Exercise
    • "Custom Functions" Exercise
    • Process Diagramming Exercise
  • notes
    • python
      • packages
        • The bigquery Package
        • The PySimpleGUI Package
        • The dotenv Package
        • The matplotlib Package
        • The requests Package
        • The altair Package
        • The gspread Package
        • The PyMySQL Package
        • The psycopg2 Package
        • The selenium Package
        • The seaborn Package
        • The pytest Package
        • The SpeechRecognition Package
        • The flask Package
        • The pandas Package
        • The spotipy Package
        • The pipenv Package
        • The nltk Package
        • The sqlalchemy Package
        • The pymongo Package
        • The plotly Package
        • The BeautifulSoup Package
        • The sendgrid Package
        • The fpdf Package
        • The autopep8 Package
        • The tweepy Package
        • The twilio Package
        • The tkinter Package
      • Python Datatypes Overview
        • Numbers
        • Classes
        • Dates and Times
        • Strings
        • None
        • Dictionaries
        • Booleans
        • Lists
        • Class Inheritance
      • Control Flow
      • Python Modules
        • The webbrowser Module
        • The time Module
        • The csv Module
        • The sqlite3 Module
        • The itertools Module
        • The json Module
        • The math Module
        • The os Module
        • The statistics Module
        • The random Module
        • The pprint Module
        • The datetime Module
        • The collections Module
      • Printing and Logging
      • Comments
      • Syntax and Style
      • Functions
      • Variables
      • Errors
      • Docstrings
      • File Management
      • User Inputs
      • Debugging
    • clis
      • The git Utility
      • Heroku, and the heroku Utility
      • Anaconda
      • The chromedriver Utility
      • The brew Utility (Mac OS)
      • The pdftotext Utility
      • The python Utility
      • The pip Utility
    • Software
      • Software Licensing
      • Software Documentation
      • Software Ethics
      • Software Testing Overview
      • Application Programming Interfaces (APIs)
      • Software Version Control
      • Software Refactoring Overview
    • devtools
      • The VS Code Text Editor
      • Code Climate
      • Travis CI
      • GitHub Desktop Software
      • Git Bash
      • Google Colab
    • Information Systems
      • Computer Networks
      • Processes
      • Datastores
      • Information Security and Privacy
      • People
    • Technology Project Management
      • Project Management Tools and Techniques
      • The Systems Development Lifecycle (SDLC)
    • hardware
      • Servers
    • Environment Variables
  • projects
    • "Executive Dashboard" Project
      • testing
      • "Exec Dash" Further Exploration Challenges
    • The Self-Directed (a.k.a "Freestyle") Project
      • "Freestyle" Project - Demonstration
      • "Freestyle" Project - Implementation (TECH 2335 Version)
      • "Freestyle" Project - Implementation
      • "Freestyle" Project Proposal
      • plan
    • "Robo Advisor" Project
      • Robo Advisor Project - Automated Testing Challenges
      • "Robo Advisor" Further Exploration Challenges
    • "Shopping Cart" Project
      • "Shopping Cart" Project - Automated Testing Challenges
      • "Shopping Cart" Further Exploration Challenges
      • "Shopping Cart" Project Checkpoints
  • License
  • Exam Prep
  • units
    • Unit 4B: User Interfaces and Experiences (Bonus Material)
    • Unit 5b: Databases and Datastores
    • Module 1 Review
    • Unit 7b: Processing Data from the Internet (Bonus Material)
    • Unit 9: Software Products and Services
    • Unit 8: Software Maintenance and Quality Control
    • Unit 7: Processing Data from the Internet
    • Unit 6: Data Visualization
    • Unit 5: Processing CSV Data
    • Unit 4: User Interfaces and Experiences
    • Unit 3: Python Datatypes
    • Unit 12: Project Presentations
    • Unit 2: Python Language Overview
    • Unit 11: Project Implementation Sprint
    • Unit 1: The Python Development Environment
    • Unit 10: Software Planning, Analysis, and Design
    • Unit 0: Onboarding
    • Unit 5B: Advanced Data Analytics
  • Contributor's Guide
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  • Setup
  • Instructions
  • Further Exploration Challenges

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  1. exercises

"Monthly Sales Predictions" Exercise

WARNING: intended for students with experience and interest in statistics

Setup

Use the same repo with monthly sales CSV data files you used for the "Monthly Sales Reporting" exercise, or make a copy of that repo called something like "monthly-sales-predictions" and add the same CSV files into that new repo.

Make sure your repo includes all available "monthly sales data" CSV files.

Instructions

Research and leverage third-party Python packages to predict top-level monthly sales for each of the next twelve months following the latest available month of available data.

Further Exploration Challenges

Can you predict the monthly sales for each item?

PreviousREADMENextSetting up your Local Development Environment

Last updated 4 years ago

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