Machine Learning
k-Nearest Neighbors Classification algorithm [Case Study]
Predicting car quality with the help of Neighbors Introduction : The goal of the blogpost is to get the beginners started with fundamental concepts of the K Nearest Neighbour Classification Algorithm popularly known by the name KNN classifiers. We will mainly focus on learning to build your first KNN model. The data cleaning and preprocessing parts would be covered in detail in an upcoming post. Classification Machine Learning is a technique of learning where a particular instance is mapped against one of the many labels. The labels are prespecified to train your model . The machine learns the pattern from the data in such a way that the learned representation successfully maps the original dimension to the suggested label/class without any more intervention from a human expert. How does...
K-Means model for Predicting Car quality[Case Study]
Problem Statement : To build a simple K-Means model for clustering the car data into different groups. Data details ========================================== 1. Title: Car Evaluation Database========================================== The dataset is available at “http://archive.ics.uci.edu/ml/datasets/Car+Evaluation” 2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 19973. Past Usage: The hierarchical decision model, from which this dataset is derived, was first presented in M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988. Within machine-learning, ...
K-Means Clustering Algorithm
Introduction : The goal of the blogpost is to get the beginners started with fundamental concepts of the K Means clustering Algorithm. We will mainly focus on learning to build your first K Means clustering model. The data cleaning and preprocessing parts would be covered in detail in an upcoming post. Clustering : Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. We can show this w...
Using Decision Trees for Regression Problems [Case Study]
Introduction : The goal of the blogpost is to equip beginners with the basics of Decision Tree Regressor algorithm and quickly help them to build their first model. We will mainly focus on the modelling side of it. The data cleaning and preprocessing parts would be covered in detail in an upcoming post. In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and what is estimated. The MSE is a measure of the quality of an estimator—it is always non-negative, and values closer to zero are better. The Mean Squared Error is given by: Enough of theory , let’s start with implementation. P...
Decision Tree model for prediction of Car quality [Case Study]
Problem Statement : To build a Decision Tree model for prediction of car quality given other attributes about the car. Data details: ========================================== 1. Title: Car Evaluation Database ========================================== The dataset is available at “http://archive.ics.uci.edu/ml/datasets/Car+Evaluation” 2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 1997 3. Past Usage: The hierarchical decision model, from which this dataset is derived, was first presented in M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988. With...
Making Intelligent decisions with Decision Trees
Introduction : In this blog we will discuss a Machine Learning Algorithm called Decision Tree. The goal of the blogpost is to get the beginners started with fundamental concepts of a Decision Tree and quickly help them to develop their first tree model in no time. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences. It is one way to display an algorithm that only contains conditional control statements. A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute , each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification ru...
Important Pillars of Stats Covariance and Correlation
Introduction : Covariance and Correlation are two mathematical concepts which are quite commonly used in statistics. When comparing data samples from different populations, Both of these two determine the relationship and measures the dependency between two random variables. Covariance and correlation show that variables can have a positive relationship, a negative relationship, or no relationship at all. A sample is a randomly chosen selection of elements from an underlying population. We calculate covariance and correlation on samples rather than complete population. Covariance and Correlation measured on samples are known as sample covariance and sample correlation. Sample Covariance : Covariance measures the extent to which the relationship between two variables is linear. The sign of ...
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