An important task to handle dataset with more number of features/dimensions.

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Reference: Medium

Data keeps on increasing every second and it has become crucial to interpreting insights from this data to solve problems. And, as features of data increases so dimensions of the dataset increases. Eventually, a Machine Learning model needs to handle the complex data resulting in more complexities. On the other hand, there are a lot of features that are futile for the model or are correlated with others. Principal Component Analysis (PCA) is the way out to reduce dimensions and deduct correlated features from the dataset.

The article is…

Data Visualization

One of the best techniques to reduce dimensions and visualize data based on probabilistic scores.

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Source: Medium

Data Visualization plays a crucial role in real-time Machine Learning applications. Visualizing data makes a much easier and convenient way to know, interpret, and classify data in many cases. And there are some techniques which can help to visualize data and reduce dimensions of the dataset.

In my previous article, I gave an overview of Principal Component Analysis (PCA) and explained how to implement it. PCA is a basic technique to reduce dimensions and plot data. There are some limitations of using PCA from which the major is, it does not group similar classes together rather it is just a…

An introduction and detailed explanation of SVM (an ML algorithm used for classification, regression problems, and outlier detection).

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Image by Clair Liu on kdnuggets

In supervised learning algorithms, labels are given to data-points (i.e- pairing a data-points with its class). In other words, models are supervised with class labels. While, in unsupervised learning algorithms, labels are not given to data-points, the model tries to find the class labels by comparing a data-point with other data-points and label similar data-points as the same class and likewise. In other words, models are not supervised explicitly.

Support Vector Machine (SVM) is a popular supervised Machine Learning algorithm used for classification problems, regression problems, and outlier detection. In simple words, when all data-points plotted in n-dimensional space (dimensions…

Machine Learning

Explanation and application of popular metrics to measure the performance of ML models.

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Image by Kiran Parte on Medium

“If you can’t measure it, you can’t improve it.” — Peter Drucker

Machine Learning and Deep Learning models have become a credible guide to many businesses, and for a good reason. These models can guide in “predicting the future circumstances” as there are numerous methods available, and any industry can fit as per the challenges and goals one has. When we talk about Machine Learning or Deep Learning models, we are either talking about Classification (discrete output) or Regression (continuous output) problems.

Table of Contents:

  1. Introduction
  2. Accuracy
  3. Confusion Matrix
  4. F1-score
  5. Receiver Operating Characteristics — ROC Curve & AUC metric
  6. Log-loss
  7. R-squared / Coefficient of Determination
  8. Mean Absolute Percentage Error (MAPE)


While data preparation and training…

Simple explanation of keys with an example that is frequently asked in interviews and is a part of DBMS beginners tutorial.

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So far, I have come across numerous articles that explain the types of Relational Database keys, and sometimes articles contain some key’s name that I haven’t even heard the name. And, I always felt confused while preparing for interviews or when someone asks questions about keys. Thus, I have prepared this article as a source with a simple explanation of each key with an example.

First, I will explain what are keys in DBMS?

KEYS in DBMS is an…

Data Analysis

Complete guide to explore & analyze NYC Airbnb dataset

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Source: Kaggle

I have started a series to explain Exploratory Data Analysis (EDA) with a particular dataset to help to understand EDA in a better way. EDA is a broad approach & it includes different ways of implementation, it varies from dataset to dataset. To know the basics of EDA, check this article as it gives an overview of EDA while this article is written to focus on the hands-on practice of EDA rather than the basics of EDA.

What is Exploratory Data Analysis (EDA)?

  • How to ensure you are ready to use machine learning algorithms in a project?
  • How to choose the most suitable algorithms for your…

Data Analysis

Introduction to D-Tale for Exploratory Data Analysis

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The goal is to turn data into information, and information into insight — Carly Fiorina

To solve any Data Science problem, it is necessary to understand raw data & somehow to convert the raw data to information for further work. Exploratory Data Analysis (EDA) is the step in which data is explored after the process of data collection. If you’re a beginner, then first be familiar with the EDA terminologies as in this article I would be covering a library namely D-Tale (can be used to make your work faster), which is used with just one line of code to…

Data Analysis

A practical guide to get started to gain insights from your data.

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“Data will talk, if you are willing to listen”- Jim Bergeson

With the proper use of data, one can gain insights and use it for numerous purposes. Raw data has no story to tell. So, to understand and gain insights from data, after the data collection process, exploratory data analysis comes into the picture. It is a crucial process to recognize patterns and understand data to prepare the model.

This article is divided into the following sections:

  1. Overview of Data Exploratory Analysis (EDA)
  2. EDA for Haberman’s dataset

Overview of Data Exploratory Analysis (EDA):

What is EDA?

The process to explore and understand data to gain insights from the data…

Rajvi Shah

I explore, learn & implement concepts in Data Science. I write to understand & make complex concepts simple.

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