Learning a new programming language is both exciting and humbling. Especially when it’s a language having as varied use cases as python. So, if you’re on your way to learning python, it’s crucial for you to try your hands on some projects. For a fresher, it makes sense to try out some mini projects before you get your hands really dirty. So, here’s a list of five mini-projects for you to improve your Python skills:
- Mad Libs Generator
This project is inspired by Summer Son’s Mad Libs project based on JavaScript. The program first prompts a user for a series of inputs a la Mad Libs. For example, an adjective, a collective noun, etc. Then, when all the information has been inputted, the program takes that data and places them into a story template.
For this, you’ll need to learn how to use prompts for the user input, and print the full story at the end with all the inputs included. This requires a command on the following concepts:
Strings
Concatenation
Variables
This one’s a pretty fun project that teaches you how to manipulate user-inputted data. As opposed to the prior projects, this is focused more on strings and their concatenations. See what crazy stories you can come up with!
2. Hangman
The actual “hangman” part isn’t really necessary. The main goal here is to create a “guess the word” kind of a game. The user will input letter guesses, you’ll need to limit the number of guesses a user can make.
This means, you’ll randomly grab a word to use for guessing – this can be done from a pre-made list. Then, you’ll need functions to see if the user has entered a singler letter and if that letter is a part of that word. And if it is, then how many times does it appear in the word.
For this, you’ll require the following concepts:
Random
Variables
Boolean
Input and Output
Integer
Char
String
Length
- Bootcamp
This will help you dive deeper in your Python programming knowledge. The framework that you’ll need here is the Django framework.
Bootcamp is basically a concept of an enterprise social network. It can be used to help developers or people belonging to one community collaborate and share experiences better. Its motive is to be closed and run inside a company only.
The whole idea will be to have a simple feed, just like Twitter. It will allow users to share links and post their thoughts and also keep track of everyone else in their community. It can also have a QnA section like StackOverflow where developers can post questions related to the business, software, or projects.
Here are a few things that you should definitely include as a part of your Bootcamp project:
– Feed App (A Twitter-like microblogging platform)
– Live feed updates
– Comments and likes
– Track comments and activities
– Articles App: this can be a collection of resource relevant to the users of the Bootcamp application. It can be a separate section other than the news feed which will contain all the necessary articles.
– QnA app (a StackOverflow-like platform)
– Messages
– Simple async messages
– Tracking of read/unread
– Search app: to help users search through their feed.
- Machine Learning Gladiator
Machine Learning Gladiator is one of the fastest ways to build practical intuition around ML.
The goal of this project will be to take the out-of-the-box models and apply them on different data sets. This project is completely amazing, especially for beginners, for three main reasons:
First, you’ll get to build intuition for model-to-problem fit. This will help you understand which models are robust to missing data? Which models handle categorical features nicely? Ofcourse you can read through the theory and find the answer, but it’s always better to learn by seeing things in action.
Second, the project will help you acquire the invaluable skill of prototyping models in real-time. When it comes to real-world problems, it’s often difficult to know which model will work best for you without trying it out.
Finally, this project helps you master the workflow of model building. You’ll get to practice:
– Importing data
– Cleaning data
– Splitting it into train/test or cross-validation sets
– Pre-processing
– Transformations
– Feature engineering
Because you’ll use out-of-the-box models, you’ll have the chance to focus on honing these critical steps.
For this project, you can use the following data sources:
UCI Machine Learning Repository – It contains a collection of more than 350 searchable datasets that cover almost every subject matter. You’ll surely find datasets that fit your need.
Kaggle Datasets – This contains more than 100 datasets uploaded byt he kaggle community. There are some really interesting datasets here, including PokemonGo spawn locations.
All of these projects are pretty basic, and can get you up and running in the world of Python programming. Further, if you find yourself stuck anywhere while trying to develop these games, do drop by at Coding Ninjas where there are courses around Python. These courses cover the concepts of Python with data structures, and are good enough to take you from ground zero to the top.
Happy learning!
To read more about different ML projects on Github, click here.
