Machine Learning -II

Posted on 11th May 2019

<-Back to Blogs

Machine learning has two objectives. 

1. Statstics - Prediction =>  Y = a + bi * x1 + b2 + ... bn*xn + error for Numerical data

2. Mathematics - Classification => 1/(1+ exp^(-Y)) for Categorical data

Statstic is of two types:

1. Descriptive Statstics - Yesterday - EDA methods

2. Inferential Statstics - Tommorow - Models

Problem statement:

iphone Sales for is as follows: 18,18,19,20,20,30,30,30,30,30,31,32,33,34,40,45,46,47,47,50,50,50,59,60,60,61

1. Summarise and visualise Customer age data

2. Increase 5% of sales followed by customers.

How to approach such question?

Find the different types of approaches & techniques you have. Do the data validation, data cleaning & data munging.

Example below:

Summary Approach:

1. Continous/Discrete Data Approach:

Numerical Data Approach - All statstic expect Count & percentage

2. Ordinal/ Nominal Data Approach:

Categorical Data Approach - Only Count & percentage.

To make the decision go with this apporach.

1. Histogram - Only for Data Quality check Only

2. Steam Leaf technique : For Summarization, Visualization & Data Quality Check

https://www.rosettacode.org/wiki/Stem-and-leaf_plot

for below values:

 From the above we can draw the charts for the histogram which is very useful in taking the decision to increase the sales.

Next let's see what is

3. Box Plot: For Summarization, Visualization & Data Quality Check

Range = (Max Value - Min Value) 

Bin = 5 (Suppose)

So, 43/5 = 8.6 is the width of the bin.

Summary: