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Ridge and Lasso Regression (L1 and L2 regularization) Explained Using Python

What is Regularization?

In an overall way, to make things normal or worthy is the thing that we mean by the term regularization. This is actually why we use it for applied AI. In the space of AI, regularization is the cycle that forestalls overfitting by debilitating engineers learning a more unpredictable or adaptable model, lastly, which regularizes or recoils the coefficients towards zero. The fundamental thought is to punish the perplexing models for example adding an unpredictability term so that it will in general give a greater misfortune for assessing complex models. 

All in all, a type of prescient displaying strategy which examines the connection between an objective variable to its indicator i.e., the autonomous variable is the thing that we know as relapse examination. Generally, this procedure is utilized for estimating, time arrangement demonstrating, and finding the causal impact connection between the factors. A continuous model is the connection between the forecast of pay of new representatives relying upon long stretches of work experience is best concentrated through relapse. 

Presently, the following inquiry emerges is the reason do we use Regression Analysis? 

Relapse examination gives us the most effortless strategy to think about the impacts of factors estimated on various reach, for example, the impact of payment changes and the quantity of impending, special exercises. These advantages help economic specialists’, information examiners’ and information researchers to wipe out and assess the best arrangement of factors to be utilized for building prescient models. 

As of now talked about above, relapse investigation assists with assessing the connection among needy and autonomous factors. How about we comprehend this with a simple model: 

Assume we need to appraise the development in deals of an organization dependent on the current monetary states of our country. The new organization information accessible with us talks that the development in deals is around multiple times the development in the economy. 

Utilizing the relapse understanding, we can undoubtedly anticipate future deals of the organization dependent on present and past data. There are different advantages of utilizing relapse investigation. They are as, for example, it gives a forecast by demonstrating the critical connections between subordinate variable and autonomous variable and portraying the strength of the effect of different free factors on a reliant variable. 

Presently, proceeding onward with the following significant part on what are the Regularization Techniques in Machine Learning. 

Regularization Techniques

There are principally two kinds of regularization methods, specifically Ridge Regression and Lasso Regression. The manner in which they relegate a punishment to β (coefficients) is the thing that separates them from one another.

Ridge Regression (L2 Regularization)

This method performs L2 regularization. The fundamental calculation behind this is to alter the RSS by adding the punishment which is equal to the square of the greatness of coefficients. Be that as it may, it is viewed as a strategy utilized when the information experiences multicollinearity (free factors are profoundly associated). In multicollinearity, yet the littlest sum squares gauges (OLS) are impartial, their fluctuations are enormous which goes amiss the noticed worth distant from truth esteem. By adding a level of predisposition to the relapse gauges, edge relapse diminishes the quality blunders. It will in general tackle the multicollinearity issue through shrinkage boundary λ. Presently, let us examine the condition underneath. 

In this condition, we have two segments. The premier one signifies the most un-square term and the later one is lambda of the summation of β2 (beta-square) where β is the coefficient. This is added to the least-square term to contract the boundary to have an extremely low difference. 

Each method has a few upsides and downsides, such as Ridge relapse. It diminishes the unpredictability of a model however doesn’t lessen the number of factors since it never prompts a coefficient tending to zero rather just limits it. Thus, this model is definitely not a solid match for include decrease.

Lasso Regression (L1 Regularization)

This regularization strategy performs L1 regularization. In contrast to Ridge Regression, it adjusts the RSS by adding the punishment (shrinkage amount) identical to the amount of the supreme estimation of coefficients. 

Taking a gander at the condition beneath, we can see that like Ridge Regression, Lasso (Least Absolute Shrinkage and Selection Operator) additionally punishes the supreme size of the relapse coefficients. Furthermore, it is very equipped for decreasing the changeability and improving the exactness of straight relapse models.

Limitation of Lasso Regression:

In the event that the quantity of indicators (p) is more noteworthy than the number of perceptions (n), Lasso will pick all things considered n indicators as non-zero, regardless of whether all indicators are significant (or might be utilized in the test set). In such cases, Lasso once in a while truly needs to battle with such sorts of information. 

On the off chance that there are at least two exceptionally collinear factors, at that point LASSO relapse select one of them arbitrarily which isn’t useful for the translation of information.

Rope relapse contrasts from edge relapse such that it utilizes total qualities inside the punishment work, as opposed to that of squares. This prompts punishing (or equally obliging the amount of the outright estimations of the appraisals) values which causes a portion of the boundary evaluations to turn out precisely zero. The more punishment is applied, the more the evaluations get contracted towards a total zero. This serves to variable choice out of the given scope of n factors.

Here is the Practical Implementation of L1 & L2 Using Python

Understanding Statistical Analysis & Its Process

Statistics have proven to be the biggest game-changer in the context of a business in the 21st century, leading to the boom of the new oil “Data”. Data is so powerful that when analyzed, it can change the fortunes of a company. Every company out there is leveraging the data to make its decisions and strategies.

But before going any further let us understand what statistics mean:

Statistics is the Science of Collecting, Presenting, Analyzing, and Interpreting any numerical data. 

Data by itself has very little value unless we can understand it, interpret it, and analyze it. A huge amount of data can be analyzed to get valuable insights, but without analysis all of this, data is just a bunch of numbers that wouldn’t make any sense at the first glance. 

After analyzing all types of data, it is then categorized into structured data and unstructured data. 

Statistical analysis is used to get meaningful information from the raw data by using different techniques such as preprocessing the data, graphical representation, and modeling techniques like (correlation, regression, ANOVA, etc).

Process of statistical analysis

Statistical analysis is a 6 step long process and takes a lot of time to crack important insights. The steps involved in the statistical analysis are as follows : 

  1. Defining the business objective of the analysis 
  2. Collection of Data 
  3. Data Visualization
  4. Data Pre-Processing 
  5. Data Modelling 
  6. Interpretation of Data 

Step 1: Defining the objective of the analysis.

The first step is to understand the reason for the analysis. Here we have to pre-decide, what we want to achieve by doing the analysis? Setting the objective is one of the most important steps of analysis; because this will work as the framework for all the next steps. 

Step 2: Collection of the data

Now, this is the most important step in the analysis process. Because here you have to collect the required data from various sources. 

There are commonly two methods/sources of data collection-

  • Primary Data– Primary data refers to the data that is being freshly collected and is not used in the past. Primary data can be collected via surveys, interviews, and personal observations.
  • Secondary Data– Secondary data refers to the pre-existing information, which has already been collected and recorded by some researchers for their own purposes and is openly available to use. Sources of secondary data are the Internet, TV, research papers, etc. 

Step 3: Data Visualization 

This step is crucial as it will help us understand the non-uniformities of the data in a data set. This will help us sort the data in a manner that will help us fill the gaps and expedite the process of analysis. Various Visualization tools like Tableau, BI, Power BI can be used for Data Visualization. 

Step 4: Data Pre-Processing 

Data preprocessing is the process of gathering, selecting, and transforming data to analyze data. It is the most time taking process as it accounts for 80% of the time taken for analysis.

Step 5: Data Modelling

After data preprocessing, the data is ready for analysis. We must choose statistical techniques like ANOVA, Regression, or any other technique based on the variables in the data.

Step 6: Interpretation:

We then come to the final step of our analysis which is Interpretation. Data interpretation means implementing various processes through which data can be reviewed to arrive at an informed conclusion. 


So far we have covered all the important topics related to statistical analysis. And you might have realized that statistical thinking involves the careful study of the cycle, collecting meaningful data to answer a concise research question, and then a detailed analysis of patterns from the data, and finally, drawing conclusions from the data.

Introduction to Data Science: A Guide For Beginners

Data Science is among the hottest jobs of the 21st century. Every company out there is looking for data scientists. Data Science is a multidisciplinary field that uses various tools and techniques to collect & organize raw, structured, or unstructured data. That data is eventually used for decision making and numerous other purposes. 

In short, Data Science is all about:

  • Collecting and analyzing raw data.
  • Modeling the data using various algorithms.
  • Visualizing the data to get a better perspective.
  • Understanding the data to make better decisions.


Let’s suppose, you want to pursue an MBA. Now, you need to make some decisions like, What are the best MBA colleges in the country, What are their fee structures, what is the admission procedure, and finally their cut-offs and entrance exams. 

All these decision factors will act as input data, and after you analyze all these things, you’ll be able to decide the best college for yourself. This analysis of data is called data analysis, which is a vital part of data science.

Why Data Science?

Data Science helps an organization to make better decisions, find gaps and opportunities, find a target audience, and even in recruiting the right talent. On top of it, as everything is backed by data it even helps to reduce business risk. 

Data Science even helps you to predict your customer’s next steps on the basis of historical trends such as his/her browsing history, engagement, & demographics. 

Check out this Liverpool Case study for better understanding:

Who Is A Data Scientist?

To put it in simple words, A Data scientist is an expert who practices Data Science. They utilize their technical and non-technical skills to collect, segregate, and structure and visualize the data. So, management can make sound decisions. 

Data scientists crack complex data problems with their strong expertise and various methodologies and algorithms.

Prerequisites To Become A Data Scientist. 

To become a data scientist you must have good communication and analytical skills. Data Scientists work with several subject matters such as mathematics, statistics, computer science, and programming languages. And as for the skills, you should have in-depth knowledge of:

  1. Machine learning.
  2. Mathematical modeling.
  3. Statistics.
  4. Computer programming.
  5. Databases.

Data Science Jobs:

As the data consumption is increasing day by day the demand for data scientists is skyrocketing the market. According to various job portals, the demand for data scientists is on a continuous rise. There are numerous opportunities in the field of Data Science. Some of the main job roles that you can get into after leaning data science are given below:

  1. Data Scientist
  2. Data Analyst
  3. Machine learning expert
  4. Data engineer
  5. Data Architect
  6. Data Administrator
  7. Business Analyst
  8. Business Intelligence Manager

How to Become a Data Scientist?

Even if you have never written code, you can still start your career as a data analyst in the field of data science. You should have a bachelor’s degree in data science or a certified course to become a Data Scientist. If you have Mathematics and Statistics as a subject background then it increases your chances of being successful in this field. 

18 Time Series Analysis Tactics That Will Help You Win in 2020

Today, various associations have gotten time game plan examination and deciding methods to develop their business strategies. These methodology help in evaluating, checking, and anticipating business examples and estimations. Time game plan assessment is profitable and is generally used for financial envisioning, yield projection, stock examinations, assessment examination, bargains assessing, protections trade examination, and budgetary examination.

What Is a Time Series?

Time plan is an organized progression of data centers spread all through some timespan. Here, time is usually a free factor while the other variable/s keep developing qualities. The time game plan data is looked at consistent common stretches. This data can be in any quantifiable and quantifiable limit related to the field of business, science, account, etc

What is Time Series Analysis?

Time Series Analysis insinuates perceiving the normal models appeared by the data all through some time frame. For this, experts use unequivocal strategies to mull over the data’s..(Read More)

Implementation Of Bag Of Words Using Python

“Language is a magnificent vehicle of correspondence” 

We, as people can without a very remarkable stretch fathom the significance of a sentence inside a limited quantity of a second. However, machines disregard to manage such messages. They need the sentences to be isolated with numerical game plans for a straightforward arrangement. 

 Bag of Words 

Bag of Words model is the technique for pre-setting up the substance by changing over it into a number/vector plan, which keeps a check of the total occasions ……[Read More]

KNN Algorithm Using R 765

The huge amount of data that we’re generating each day , has led to an increase of the need for advanced Machine Learning Algorithms.
It is quite essential to know Machine Learning basics. Here’s a fast introductory section on what’s Machine Learning and its types.

Machine learning could also be a subset of AI that provides machines the power to hunt out out automatically and improve from their gained experience without being explicitly programmed.

There are mainly three kinds of Machine Learning discussed briefly below:

Supervised Learning: it’s that a neighborhood of Machine Learning during which the data provided for teaching or training the machine is well labeled then it becomes easy to work with it.

Unsupervised Learning: it is the training of knowledge employing a machine that’s unlabelled and allowing the algorithm to act thereon information without guidance.

Reinforcement Learning: it’s that a neighborhood of Machine Learning where an agent is put in an environment and he learns to behave by performing certain actions and observing the numerous possible outcomes which it gets from those actions.

Now, moving to our main blog topic,

What is KNN Algorithm?
KNN which stands for K Nearest Neighbor could also be a Supervised Machine Learning algorithm that classifies a replacement datum into the target class, counting on the features of its neighboring data points.

Let’s decide to understand the KNN algorithm with an essay example. Let’s say we might sort of a machine to differentiate between the sentiment of tweets posted by various users. to undertake to to the present we must input a dataset of users’ sentiment(comments). And now, we’ve to teach our model to detect the emotions supported certain features. as an example , features like labeled tweet sentiment i.e., as positive or negative tweets accordingly. For positive tweet, it’s labeled as 1 and for negative, it,’s labeled as 0.

Features of KNN algorithm:

KNN could also be a supervised learning algorithm, supported feature similarity.

Unlike most algorithms, KNN could also be a non-parametric model which suggests it doesn’t make any assumptions about the data set. which makes the algorithm not only simpler but also effective because now it can handle realistic data.

KNN is taken under consideration to be a lazy algorithm, i.e., it suggests that it memorizes the training data set rather than learning a discriminative function from the training data.

KNN is typically used for solving both classification and regression problems.

Disadvantages of KNN algorithm:

After multiple implementations, it has been observed that KNN algorithm doesn’t work with good accuracy on taking large datasets because the worth of calculating the space between the new point and each existing points is large , and successively it degrades the performance of the algorithm.

Disadvantages of KNN algorithm:

It has also been noticed that performing on high dimensional data is quite difficult with this algorithm because the calculation of the space in each dimension isn’t correct.

It is quite needful to perform feature scaling i.e., standardization and normalization before actually implementing KNN algorithm to any dataset. Eliminating these steps may cause wrong predictions by KNN algorithm.

Sensitive to noisy data, missing values and outliers: KNN is sensitive to noise within the dataset. we’d wish to manually impute missing values and deduct outliers.

Resource Box

we hope we got the detail idea about KNN algorithm through this blog. This blog is inspired by Excelr Solution. 

Difference Between Correlation and Covariance

Correlation vs Covariance

 Connection and Covariance are closely related terms but there are lot to differ when we need to choose one of these.  Connection and Covariance are two normally utilized factual ideas significantly used to gauge the direct connection between two factors in information. At the point when used to think about examples from various populaces, covariance is utilized to recognize how two factors fluctuate together while connection is utilized to decide how change in one variable is influencing the adjustment in another variable. Despite the fact that there are sure likenesses between these two scientific terms, these two are unique in relation to one another. Peruse further to comprehend the distinction among covariance and relationship.


It is a pointer of how much two factors change concerning each other i.e.., it gauges the heading of straight connection between these two factors .The estimations of covariance can lies in the scope of – ? to +?

Xi – values of X variable

Yj – values of Y variable

X?- mean of x variable

Y?- mean of y variable

N- Number of data points ( n-1 for sample covariance)


Correlation defines the strength and direction of linear relationship between two variables otherwise you can simply say that it is a normalized version of covariance. so when you divide the covariance with standard deviation of the variables, it scales down the range to -1 to +1 , comparatively correlation values are more interpretable.

Now let’s see see the difference between Correlation and Covariance:

one of major difference in these two is Covariance is influenced by the change in scale but in case of correlation values are not influenced by change in scale. rest we can with below table:- 


This blog is inspired by Excelr Solutions .   If you are interested to know the calculation of same terms listed above by using python with inbuilt function visit here correlation vs covariance

Concept of Simple Linear Regression

Simple linear regression is used to estimate the relationship between two variables, where the variables are continuous in nature.  so it used to define relation between single input variable and single output variable and also defines how this relation can be presented by straight line.

Below plot shows the graphical relation between two continuous variable.

This scatter shows three terms – 

  1. The direction
  2. The strength
  3. The linearity

 Here plot shows that variable x and y shares positive liner relationship.  So the most exact way to define this data is straight line.if relationship between two variables x and y stands strong then we can predict output variable y on the basic of input x variable nature. And this can be represented by straight line.Now we have correlation coefficient (r) to check collinearity between variable X and Y.
correlation coefficient (r)stands for numerical value of correlation between two variables. if value of r is higher then it means that the input variable x is good for y.
During this we have to count on some properties of ‘r’, listed below-

  1. Range of r: -1 to +1
  2. Perfect positive relationship: +1
  3. Perfect negative relationship: -1
  4. No Linear relationship: 0
  5. Strong correlation: r > 0.85 (depends on business scenario)

Here, Command used for calculation “r” in RStudio is:

> cor(X, Y)


 X: independent variable 

 Y: dependent variable

 Now, there are two conditions depends on value of ‘r’ ,i.e result of above equation.

case 1- if r > 0.85 then choose simple linear regression and 

case 2- If r < 0.85 then use transformation of data to increase the value of “r” and then build a simple linear regression model on transformed data.

There are four steps to Implement Simple Linear Regression:

  1. Analyze data (analyze scatter plot for linearity)
  2. Get sample data for model building
  3. Then design a model that explains the data
  4. And use the same developed model on the whole population to make predictions.

The equation that represents how an independent variable X is related to a dependent variable Y.

For Example:

Let’s Consider we want to calculate the weight gain based upon calories taken. And for this we have below data. 

here we want to know weight gain when you consume 2500 calories. First, we need to draw a  graph of the data which will show that calories consumed is  independent variable X to predict dependent variable Y.

here “r”  can be calculated as follows:

As mentioned above case 1 here, r = 0.9910422 which is greater than 0.85, we can consider calories consumed as the best independent variable(X) and weight gain(Y) as the predict dependent variable.

Now, if we  try to draw a line  in a way that it should be close to every data point in the above plot diagram. It will be like this-

To calculate the weight gain for 2500 calories, simply extend the straight line further to the y-axis at a value of 2,500 on x-axis . This projected value of y-axis gives you the rough weight gain. This straight line is a regression line.

Similarly, if we substitute the x value in equation of regression model such as:

y value will be predicted.

Following is the command to build a linear regression model.

We obtain the following values

Substitute these values in the equation to get y as shown below.

So, weight gain predicted by our simple linear regression model is 4.49Kgs after consumption of 2500 calories.

Resource Box

we hope we got the detail idea about Simple Liner Regression through this blog. This blog is inspired by Excelr Solution. 



Data Analysis is the process that involves the collection, transformation, and cleaning. It also deals with the modeling of data with the objective of discovering and identifying the required information. The results obtained henceforth are then communicated with suggestions for conclusions to support decision-making. At certain times, the visualization of data is used for the purpose of portraying the necessary data and information, which thereby eases the discovery of useful patterns within the data that has been obtained. Data Analysis and Data Modelling are exactly identical terms used in the field of big data.


The term ‘analysis’ in the term ‘data analysis’ refers to the procedure of breaking something into its separate individual components for the purpose of individual scrutiny and examination. Data analysis is actually the process of obtaining the raw data and thereby converting it into useful and important information, which is used for decision making by the users. The data, which has been collected, is analyzed to answer questions, disprove theories, or test hypotheses. 

There are multiple and numerous approaches and facets in data analysis, which encompasses diverse methodologies under various names that are used in different types of scientific, business, and socially scientific domains. In the modern era, data analysis plays an extremely important and essential role in making any form of decision that is much more scientific than without its implementation. For this reason, it helps the respective business enterprises to conduct their proceedings and operate much more effectively than ever before.


  • Confirming the fact that the main total is the sum of the subtotals
  • Checking the relationship between the numbers
  • Checking the raw data files for abnormalities prior to the performance of the user’s analysis
  • Re-performing important calculations, such as the process of verifying the columns containing the respective data which are formula driven
  • Normalizing the numbers for the purpose of making the process of comparison easier. It includes analyzing the amount per person, anything related to GDP, or as the index value which is relative to the base year
  • Breaking the problems into separate individual components by analyzing the factors like the DuPoint analysis of the return on equity. 
  • Identifying the areas for the purpose of increasing the efficiency and the automation of processes.
  • Setting up and maintaining automated data processes.
  • Identifying, evaluating as well as implementing external tools and services for the purpose of supporting cleansing and data validation.
  • Creating graphs, dashboards, and visualizations.
  • Providing competitor and sector benchmarking
  • Analyzing and mining large sets of data, drawing valid influences, and presenting them successfully to the management with the use of a reporting tool.
  • Designing and carrying out surveys as well as analyzing the survey data
  • Manipulating, analyzing, and interpreting complex sets of data that are related to the business of the employer.
  • Preparing reports for the external and internal audience through the use of business analytics reporting tools.

Inference: Data analysis and data analytics play an extremely vital role in the invention, manufacture, and production of technologies and products of the next generation. It is due to this reason that data analytics has gained so much importance in the private sector and various Multinational Companies (MNCs).    

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People can pursue a career in data analytics with any kind of degree or subject if they possess the relevant skills and requirements for filling the respective posts. Postgraduate degrees in the field of data science is getting more popular day by day.

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Phone: 096321 56744

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Data Analytics, Types and its Advantages

The use of software and specialized system to examine data in order to draw conclusions about it in various aspects is called Data Analytics. Data Analytics is widely used by commercial companies nowadays. It helps the organizations to run the administration more efficiently. It also helps them to make a more appropriate business-related decision. It also helps the researchers, scientists to verify or approve the hypothesis and theory. Data Analysts include various applications under it. Some of the applications under them are business intelligence (BI), online analytical process (OLAP) and advanced analytics.

Advantages of Data Analytics

There are several benefits of Data analysis. It helps us to increase operational efficiency, increasing the business revenues to a very high level, developing good marketing campaigns and also providing better service efforts to the customers. It can also be used for real-time analytics. The real-time analytics include the new as well as old information in the field of Data Analytics Courses

Types of Data Analytics Application

Data Analytics chiefly includes data analytics methodologies, including exploratory data analysis. The confirmatory data analysis also falls in the same category. It is widely used to find out whether the data is real or fake. It can also be distinguished as quantitative and qualitative data analysis. Quantitative data analysis involves the analysis of the mathematical data, whereas qualitative one involves understanding the nonnumerical data. Nonnumerical data includes text, pictures and also audio, video.

Advanced types of Data Analytics

Data mining, Predictive analysis, machine learning, and text mining are some examples of advanced data analysis. Data mining is identifying patterns and trends by sorting large data. Machine learning is another advanced method in which artificial intelligence technology is used by data scientists to go through the data sets. Text mining is also a data analytical process of analyzing the emails, documents, and other contents.

The inside process under Data Analytics

The data analysis is much more than analyzing data. Mainly, in the advanced analytics projects, much of the required work takes a plane in front of us. Preparing and collection of data, then preparing and integrating the data, later testing and revising the analytic process to ensure maximum accuracy of the result. The process starts with the collection of data only. Data Scientist identifies the information which they need for a definite analytic process. Then the IT sector and data engineers take over it. They prepare the desired contents. Once the desired data is made, the next task is to fix the quality of the data. The main work now is to fix the data quality. Now, data cleansing job comes into action. After this, additional data is also prepared to manipulate the previous one if needed. This is the main turning point. The data analysts build a model of it using programming languages such as Python, R language and SQL. Then it is again rechecked before further steps.

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Communication through Data Analytics

Data Analytics machines are often set to automatically do all business actions. The last step of the Data Analytics is Data Visualization. It is the process by which the desired results are communicated to the business heads. They are mainly incorporated into the Business Intelligence dashboard that displays all data on a single screen. It can also be updated in real time when needed. This is how the information becomes visible in Data Analytics.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

               Phone: 096321 56744

               Hours: Sunday – Saturday 7AM – 11PM