Saurabh Parab

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Money you saved in Quarantine.

- August 29, 2020

- By Saurabh Parab It is possible to cut back While more than a third of Indians are worried about their finances as a result of the COVID-19 crisis, others have experienced a significant boost to savings. At the end of the day, it's all about the mentality. Whether you want to believe it or not, lockdown because of the COVID-19 pandemic has led to savings that are othe rwise not noticed by us. Inability to save, worries about economic conditions, and the struggle to pay household bills are the top concerns of Indian adults during the lockdown. But while many households are struggling, some have been able to save more than usual in recent months. Why? Perhaps because we’re spending less. Ahold on holidays and Movies. Unsurprisingly, spending on non-essential items fell as a result of government-imposed lockdown. So, Indirectly the government is giving us the people some guidance about money savings. Sarcasm!!!, Anyways Analysts suggests that a typical Indian household could be

Netflix Consumption Analysis

- August 29, 2020

Netflix probably has become your go-to application when bored in this quarantine. So it's not surprising that it picked up nearly 16 million global subscribers during the first three months of the year, helping cement its status as one of the world's most essential services in times of isolation or crisis. As of April 2020, Netflix had over 193 million paid subscriptions worldwide, including 73 million in the United States It is available worldwide except China, DPRK (North Korea), Crimea. So let's do some analysis on Netflix Following is the content distribution of Netflix. ( shown in the form of a pie chart ) Next up let's compare the two powerhouses against each other i.e USA and India. As you can see the number of movies and tv shows added by the USA is constantly increasing whereas in India there's a slight decline in the addition of movies and the number of tv shows has remained constant. Viewing Time vs Hours per daily The entire world is streaming more t

Outliers Explained - By Saurabh

- August 27, 2020

What are Outliers?? Outliers are defined as samples that are significantly different from the remaining data. Those are points that lie outside the overall pattern of the distribution. Statistical measures such as mean, variance, and correlation are very susceptible to outliers. A simple example of an outlier is here, a point that deviates from the overall pattern. Outliers can occur in the dataset due to one of the following reasons:- 1. Genuine extreme high and low values in the dataset 2. Introduced due to human or mechanical error 3. Introduced by replacing missing values. In some cases, the presence of outliers are informative and will require further study. For example, outliers are important in use-cases related to transaction management where an outlier might be used to identify potentially fraudulent transactions. How to Detect Outliers?? 1.Extreme Value Analysis by BoxPlot 2.K Means clustering-based approach 3.Visualizing the data How to Treat Outliers? 1.Mean/Median or ra

Linear Regression Implementation By : Saurabh

- August 04, 2020

We discussed the Linear Regression concept in the previous blog with a Layman example. So, let's discuss the Linear Regression model and its mechanism. Linear Regression model makes use of two sets of variables:- Independent variables (termed as x ) Dependent variable(termed as y) Independent variables are the ones that are used to predict(calculate) the dependent variable. If we take into consideration an example from the previous blog, then the independent variable will be 'Kilometers' and the dependent variable will be "Amount". So in a nutshell:- You are predicting the amount based on the kilometers .i.e 'if I want travel x km then how much will it cost me (Y)? Linear Regression can be further broken down into two types:- Simple Linear Regression Multiple Linear Regression Simple Linear Regression is the one we just discussed above where we have one independent variable (km) and based on that we have to predict the dependent variable (Amount). Multi

Assumptions of Linear Regression Explained :- By Saurabh

- July 30, 2020

Before Implementing a Linear Regression model,its important to go through the assumptions.As linear Regression is a parametric algorithm, It's important that the data satisfies the following assumptions So lets go through it one by one. 1.Linearity This assumes that there is a linear relationship between the predictors (e.g independent variable) and the response variable(e.g dependent variable). You can use the scatter plot to detect the linearity of the variables. 2.No Outliers There should be no outliers in the data.You can check for outliers with the help of box plot 3. No Multicollinearity There should be no multicollinearity between the independent variables. 4. Autocorrelation There should be no correlation between the residual (error) terms.Absence of this concept is known as autocorelation. 5. Normality The dependant varible should be normally distributed.If not you convert it through log function. It’s not uncommon for assumptions to be violated on real-world data, but it

Machine Learning Algorithms Explained -By Saurabh

- July 25, 2020

Welcome back on our journey to learn the basics of Machine learning.Before having a hands on on real time data I thought making you all familiar with various types of machine learning algorithms.Let as now breakdown the classification of Machine Learning. Let us go through each type one by one. 1. Supervised Learning : Imagine you are a student in a class.Teacher trains you by giving you knowledge about a subject. Supervised learning is like learning from a teacher here the machine is you(student) and the teacher is the training data(the data we discussed in the previous blog, one stored in the excel file).Training data will be used to train the model. Supervised Learning is further divided into two types:- Regression Classification Regression includes a scenario where machine is trained to predict some value like price ,height and weight other example will be predict stock market prices etc.Algorithms which can perform regression are Linear Regression,Decision Tree, S

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