rhoit

import sklearn.tree
import sklearn.metrics
import matplotlib.pyplot as plt
import sklearn.tree
import sklearn.preprocessing
import pandas as pd

df = pd.read_csv('./data/cricket.csv')
labelEncoder = sklearn.preprocessing.LabelEncoder()
features = ['outlook', 'temperature', 'humidity', 'wind']

rhoit

#!../venv/bin/python

import json
import math
import collections

import pandas as pd


def entropy(target_col):

rhoit

import sklearn.tree
import sklearn.preprocessing
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('data/movie.csv')
labelEncoder = sklearn.preprocessing.LabelEncoder()
fig, axes = plt.subplots(1, 2, layout='tight')
for i, name in enumerate(('popcorn', 'coke')):
    decisionTreeClassifier = sklearn.tree.DecisionTreeClassifier(

rhoit

rhoit

rhoit

rhoit

#!/usr/bin/python

import argparse
import itertools
import collections


sample = [
    ['Butter', 'Bread', 'Milk'],
    ['Bread', 'Milk'],

rhoit

#!/usr/bin/python

import argparse
import itertools
import collections


def get_frequent_itemsets(transactions, min_support):
    # Count individual items
    freq = collections.defaultdict(int)

rhoit

rhoit

#!../venv/bin/python

import pandas as pd
import mlxtend.preprocessing
import mlxtend.frequent_patterns

# 1. Prepare the dataset (list of lists)
dataset = [
    {'Butter', 'Bread', 'Milk'},
    {'Bread', 'Milk'},