In this article, I will explain the basics of Time Series Forecasting and demonstrate, how we can implement various forecasting models in Python.

Forecasting is a word we usually associate with the weather. While we listen to, or watch, the NEWS, there is always a separate segment called ‘Weather Report’ where the NEWS commentator provides us with the weather forecast information. Why is forecasting so important? Well, simply because we can make **informed decisions.**

Now, there are two main types of forecasting methods, namely, Qualitative Forecasting and Quantitative Forecasting.

In Qualitative Forecasting, the forecasting decisions are dependent upon expert opinions…

A Decision Tree is a Supervised Machine Learning algorithm that imitates the human thinking process. It makes the predictions, just like how, a human mind would make, in real life. It can be considered as a series of if-then-else statements and goes on making decisions or predictions at every point, as it grows.

A decision tree looks like a flowchart or an inverted tree. It grows from root to leaf but in an upside down manner. We can easily interpret the decision making /prediction process of a decision tree, due to it’s structure.

In this article, I have explained two popular clustering algorithms, K-Means Clustering and Hierarchical Clustering, in detail, with their implementation in Python.

An unsupervised algorithm, is the one which does not use past data with labels to make predictions.

Clustering is a popular Unsupervised Machine Learning Algorithm. Here, observations with similarities are grouped together to form a **cluster**. The basic idea of clustering involves segmenting data such that, the observations with similarities are grouped together. The segments so formed should be stable; meaning two groups cannot contain same observations or same data points.

We will see how clustering works, it’s…

Ed-tech companies are becoming very popular by each passing day. They have transformed the very idea of education that once used to be sourced only through classrooms. After the world was hit by the present pandemic, not only are these companies getting good figures in terms of investments, but the pressure to create engaging and highly-personalized learning experience is also tremendous.

In this article, we will look at one such ed-tech company which sells online courses and is facing a problem of the lead conversion rate being very low. So how can Machine Learning solve this problem !! …

In this article, I have tried to explain the Logistic Regression algorithm and the mathematics behind it, in the simplest possible way.

In this article, I present how a Machine Learning algorithm can help Bike Sharing providers to boost up their revenue numbers.

Bike sharing systems are gaining popularity worldwide, due to the flexibility that they provide to hire and return the bikes. You can hire a bike from a bike sharing provider, from your convenient location, for a short period and return it to any other station (also called dock) belonging to the provider as per your convenient location.

This is essentially a self-service and there are options at many places to access it via a mobile app. These services are…

In this article, I will explain various mathematical concepts related to Linear Regression in the simplest possible way.

Linear Regression is a Machine Learning algorithm that falls under Supervised Learning method where historic data is labelled and used to determine the value of the output/dependent variable based on the predictor/independent variable/s. Here as the name suggests, the relationship between the dependent and independent variables is assumed to be **linear.**

There are 2 types of Linear regression algorithms based upon number of predictor variables:

**Simple Linear Regression:** Only one predictor variable is used to predict the values of dependent variable. Equation…

I have tried to explain Linear Regression in easiest possible way along with example.

Regression is defined as a statistical method that attempts to determine a relationship between two or more correlated variables. It is used to predict value of a variable (also called dependent variable), given the values of other variable/s (also called predictor variable/s).

The regression algorithm falls under Supervised Learning method where historic data is labelled and used to determine the value of the output variable.

If two numerical variables are linearly correlated, we will have their correlation coefficient value that falls between -1 and 1. When…

We all know the Titanic story. How the ‘unsinkable’ ship carrying hundreds of passengers met a dreadful end on April 15, 1912. But the incident still is a subject of analysis and study even after 100 years.

I am sure everyone reading this article has watched the movie Titanic. It is one of my favorites; especially the background score by James Horner and the Celine Dion song, ‘My heart will go on’ .

So I decided to download the data set and do some analysis on the same by using some interesting and colorful plots using seaborn.

Here we go…

As per Wikipedia, **IMDb** (an acronym for **Internet Movie Database**) is an online database of information related to films, television programs, home videos, video games, and streaming content online — including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews.

We have the data for the 100 top-rated movies from the past decade along with various pieces of information about the movie, its actors, and the voters who have rated these movies online. We will try to find some interesting insights into these movies and their voters, using Python.

We load the dataset with…