CharlesRajendran

function sum(num) {
  return function(b) {
    if (b) {
      return sum(num+b);
    } else {
      return num;
    }
  }
}

CharlesRajendran

// generating questions of some complexity for kids math
// ["5 + 3 =", "3 + 2 =", "0 + 3 =", "2 + 0 =", "1 + 2 ="]

function generateRandomInteger(complexity) {
  return Math.floor(Math.random() * complexity);
}

function matchingPairs(complexity) {
  const x = generateRandomInteger(complexity);
  const y = generateRandomInteger(complexity);

CharlesRajendran

// generating questions of some complexity for kids math
// ["8 + 8 =", "8 + 5 =", "6 + 5 =", "3 + 9 =", "4 + 8 ="]

function generateRandomInteger(complexity) {
  return Math.floor(Math.random() * complexity);
}

function matchingPairs(complexity) {
  const x = generateRandomInteger(complexity);
  const y = generateRandomInteger(complexity);

CharlesRajendran

const profile = {
  name: "Madasaamy",
  age: 80,
  kids: [
    {
      name: "Soori",
      age: 57,
      kids: [
        {
          name: "Miller",

CharlesRajendran

console.log('Hello World Brother')

CharlesRajendran

import pandas as pd
dataset = pd.read_csv("data.csv", header=None)

# apriori expects the input as list of list
# [ [item1, item2], [item2, item3, item4], ..... ] ]
transactions = []
for i in range(0, len(dataset)):
    transactions.append([str(dataset.values[i, j]) for j in range(0, 20)])
  
from apyori import apriori

CharlesRajendran

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('Data.csv')
X = data.iloc[:, 1:3]

# use elbow mwthod to find optimal number of clusters
from sklearn.cluster import KMeans
'''
wcss = []

CharlesRajendran

#plot the scatters
'''
X[y_pred==0] - will list records which belongs to cluster 0
output:
       Qty  UnitPrice
1     1911       3.39
'''

plt.scatter(X[y_pred == 0].iloc[:, 0], X[y_pred == 0].iloc[:, 1], s=5, c="red")
plt.scatter(X[y_pred == 1].iloc[:, 0], X[y_pred == 1].iloc[:, 1], s=5, c="green")

CharlesRajendran

kmeans = KMeans(n_clusters=4, init="k-means++", max_iter=1000, n_init=10)
y_pred = kmeans.fit_predict(X)

CharlesRajendran

X = data.iloc[:, 1:3]

# use elbow mwthod to find optimal number of clusters
from sklearn.cluster import KMeans

# with in cluster sum of squares
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters =i, init="k-means++", max_iter=300, n_init=10)
    kmeans.fit(X)