Python Fruit Machine

Hello Python Forum, as a learning experience I created a very basic 'Slot' machine. It's far from perfect and could definitely use many upgrades, more winning options, pay tables, etc. As for this project I was mostly focusing on functionality, and I may continue working on it from time to time to make it better (more interesting to play). Python Tutorial – A Complete Guide to Learn Python Programming; Programming With Python Tutorial; Python Programming Language – Headstart With Python Basics; Let’s move ahead and start building the game. I have used PyCharm with Python version 2.7 for this article. Start Building The Game. Let’s understand this game before we start.

In this challenge we are going to write a Python program that automatically generates six random numbers (from 1 to 50) and display them on the screen, sorted in ascending order.

The program will need to make sure that each number is unique; the same number cannot come twice in the selection of six selected numbers.

Learning Objectives

By completing this code, you will understand the difference between the two types of loops used in Python:

• For Loops (Count-Controlled Loops): A count-controlled loop is one that is executed a certain number of times. In our example, because we know that we need exactly six numbers we are going to use a for loop to repeat (iterate through) some of our code exactly six times.
• While Loops (Condition-Controlled Loops): A condition-controlled loop such as a While loop keeps going while a certain condition is true. As soon as the condition becomes false, the loop stops iterating. In our case, when generating a random number, we are going the check that the new number has not already been picked up. If it has then we are going to generate a new one and will carry on doing so till we get a number that has not already been picked up.

Check the code

Your Challenge

1. Complete this code to store your own selection of six numbers. For testing purposes, you can store these numbers directly into the code and display these to the end-user. Alternatively you can ask the end-user to type six numbers.
2. Then your code will use the program given above to generate the six lottery numbers.
3. Finally your code will check and inform the end-user of how many numbers the user guessed correctly by comparing the user’s six numbers with the six randomly generated lottery numbers!

Note

The probability of guessing several lottery numbers is very low. You may want to tweak your program to only pick up numbers between 1 and 20 (instead of 50). This will give you a better chance to guess some of these numbers and hence test whether your code is working.

Video Tutorial

Hey guys!! In this tutorial, we will learn about apriori algorithm and its implementation in Python with an easy example.

What is Apriori algorithm?

Apriori algorithm is a classic example to implement association rule mining. Now, what is an association rule mining? Association rule mining is a technique to identify the frequent patterns and the correlation between the items present in a dataset.

For example, say, there’s a general store and the manager of the store notices that most of the customers who buy chips, also buy cola. After finding this pattern, the manager arranges chips and cola together and sees an increase in sales. This process is called association rule mining.

More information on Apriori algorithm can be found here: Introduction to Apriori algorithm

Working of Apriori algorithm

Apriori states that any subset of a frequent itemset must be frequent.
For example, if a transaction contains {milk, bread, butter}, then it should also contain {bread, butter}. That means, if {milk, bread, butter} is frequent, then {bread, butter} should also be frequent.

The output of the apriori algorithm is the generation of association rules. This can be done by using some measures called support, confidence and lift. Now let’s understand each term.

Support: It is calculated by dividing the number of transactions having the item by the total number of transactions.

Confidence: It is the measure of trustworthiness and can be calculated using the below formula.
Conf(A => B)=

Lift: It is the probability of purchasing B when A is sold. It can be calculated by using the below formula.
Lift(A => B)=
1. Lift(A => B) =1 : There is no relation between A and B.
2. Lift(A => B)> 1: There is a positive relation between the item set . It means, when product A is bought, it is more likely that B is also bought.
3. Lift(A => B)< 1: There is a negative relation between the items. It means, if product A is bought, it is less likely that B is also bought.

Now let us understand the working of the apriori algorithm using market basket analysis.
Consider the following dataset:

Transaction ID Items
T1 Chips, Cola, Bread, Milk
T2 Chips, Bread, Milk
T3 Milk
T4 Cola
T5 Chips, Cola, Milk
T6 Chips, Cola, Milk

Step 1:
A candidate table is generated which has two columns: Item and Support_count. Support_count is the number of times an item is repeated in all the transactions.
Item Support_count
Chips 4
Cola 4
Bread 2
Milk 5
Given, min_support_count =3. [Note: The min_support_count is often given in the problem statement]

Step 2:
Now, eliminate the items that have Support_count less than the min_support_count. This is the first frequent item set.
ItemSupport_count
Chips 4
Cola 4
Milk 5

Fruit Machine Python Program

Step 3:
Make all the possible pairs from the frequent itemset generated in the second step. This is the second candidate table.
Item Support_count
{Chips, Cola} 3
{Chips, Milk } 3
{Cola, Milk} 3
[Note: Here Support_count represents the number of times both items were purchased in the same transaction.]

Step 4:
Eliminate the set with Support_count less than the min_support_count. This is the second frequent item set.
Item Support_count
{Chips, Cola} 3
{Chips, Milk } 3
{Cola, Milk} 3

Step 5:
Now, make sets of three items bought together from the above item set.
ItemSupport_count
{Chips, Cola, Milk} 3

Since there are no other sets to pair, this is the final frequent item set. Now to generate association rules, we use confidence.
Conf({Chips,Milk}=>{Cola})= = 3/3 =1
Conf({Cola,Milk}=>{Chips})= 1
Conf({Chips,Cola}=>{Chips})= 1

The set with the highest confidence would be the final association rule. Since all the sets have the same confidence, it means that, if any two items of the set are purchased, then the third one is also purchased for sure.

Implementing Apriori algorithm in Python

Problem Statement:
The manager of a store is trying to find, which items are bought together the most, out of the given 7.
Below is the given dataset

Dataset

Before getting into implementation, we need to install a package called ‘apyori’ in the command prompt.

Python Fruit Machine For Sale

1. Importing the libraries
   
2. Loading the dataset
   
3. Display the data
   
4. Generating the apriori model
   
5. Display the final rules
   

The final rule shows that confidence of the rule is 0.846, it means that out of all transactions that contain ‘Butter’ and ‘Nutella’, 84.6% contains ‘Jam’ too.
The lift of 1.24 tells us that ‘Jam’ is 1.24 times likely to be bought by customers who bought ‘Butter’ and ‘Nutella’ compared to the customers who bought ‘Jam’ separately.

This is how we can implement apriori algorithm in Python.

1. This tutorial is really shallow. Importing an implementation != implementing.

Fruit Machine Code Python