## What is Data Science?

Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.

By combining aspects of statistics, computer science, applied mathematics, and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge.

## How would you create a taxonomy to identify key customer trends in unstructured data?

The best way to approach this question is to mention that it is good to check with the business owner and understand their objectives before categorizing the data. Having done this, it is always good to follow an iterative approach by pulling new data samples and improving the model accordingly by validating it for accuracy by soliciting feedback from the stakeholders of the business. This helps ensure that your model is producing actionable results and improving over the time.

## Python or R – Which one would you prefer for text analytics?

The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high performance data analysis tools.

## Explain what regularization is and why it is useful.

Regularization is the process of adding a tuning parameter to a model to induce smoothness in order to prevent overfitting.

This is most often done by adding a constant multiple to an existing weight vector. This constant is often either the L1 (Lasso) or L2 (ridge), but can in actuality can be any norm. The model predictions should then minimize the mean of the loss function calculated on the regularized training set.

Xavier Amatriain presents a good comparison of L1 and L2 regularization here, for those interested.

## How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.?

Proposed methods for model validation:

- If the values predicted by the model are far outside of the response variable range, this would immediately indicate poor estimation or model inaccuracy.
- If the values seem to be reasonable, examine the parameters; any of the following would indicate poor estimation or multi-collinearity: opposite signs of expectations, unusually large or small values, or observed inconsistency when the model is fed new data.
- Use the model for prediction by feeding it new data, and use the coefficient of determination (R squared) as a model validity measure.
- Use data splitting to form a separate dataset for estimating model parameters, and another for validating predictions.
- Use jackknife resampling if the dataset contains a small number of instances, and measure validity with R squared and mean squared error (MSE).

## Which technique is used to predict categorical responses?

Classification technique is used widely in mining for classifying data sets.

## What is logistic regression? Or State an example when you have used logistic regression recently?

Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

## How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?

Often it is observed that in the pursuit of rapid innovation (aka “quick fame”), the principles of scientific methodology are violated leading to misleading innovations, i.e. appealing insights that are confirmed without rigorous validation. One such scenario is the case that given the task of improving an algorithm to yield better results, you might come with several ideas with potential for improvement.

An obvious human urge is to announce these ideas ASAP and ask for their implementation. When asked for supporting data, often limited results are shared, which are very likely to be impacted by selection bias (known or unknown) or a misleading global minima (due to lack of appropriate variety in test data).

Data scientists do not let their human emotions overrun their logical reasoning. While the exact approach to prove that one improvement you’ve brought to an algorithm is really an improvement over not doing anything would depend on the actual case at hand, there are a few common guidelines:

- Ensure that there is no selection bias in test data used for performance comparison
- Ensure that the test data has sufficient variety in order to be symbolic of real-life data (helps avoid overfitting)
- Ensure that “controlled experiment” principles are followed i.e. while comparing performance, the test environment (hardware, etc.) must be exactly the same while running original algorithm and new algorithm
- Ensure that the results are repeatable with near similar results
- Examine whether the results reflect local maxima/minima or global maxima/minima

One common way to achieve the above guidelines is through A/B testing, where both the versions of algorithm are kept running on similar environment for a considerably long time and real-life input data is randomly split between the two. This approach is particularly common in Web Analytics.

## What are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

## Why data cleaning plays a vital role in analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

## Differentiate between univariate, bivariate and multivariate analysis.

These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.

If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.

Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

## What do you understand by the term Normal Distribution?

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell shaped curve. The random variables are distributed in the form of an symmetrical bell shaped curve.

## What are the important skills to have in Python with regard to data analysis?

The following are some of the important skills to possess which will come handy when performing data analysis using Python.

- Good understanding of the built-in data types especially lists, dictionaries, tuples and sets.
- Mastery of N-dimensional NumPy arrays.
- Mastery of pandas dataframes.
- Ability to perform element-wise vector and matrix operations on NumPy arrays. This requires the biggest shift in mindset for someone coming from a traditional software development background who’s used to for loops.
- Knowing that you should use the Anaconda distribution and the conda package manager.
- Familiarity with scikit-learn.
- Ability to write efficient list comprehensions instead of traditional for loops.
- Ability to write small, clean functions (important for any developer), preferably pure functions that don’t alter objects.
- Knowing how to profile the performance of a Python script and how to optimize bottlenecks.

The following will help to tackle any problem in data analytics and machine learning.

## Compare Data Science Vs. Machine Learning

Criteria |
Data Science |
Machine Learning |

Scope | Multidisciplinary | Training machines |

Artificial Intelligence | Loosely integrated | Tightly integrated |

Role | Can take on a business role | Purely technical role |

## What is a Recommender System?

A recommender system is today widely deployed in multiple fields like movie recommendations, music preferences, social tags, research articles, search queries and so on. The recommender systems work as per collaborative and content-based filtering or by deploying a personality-based approach. This type of system works based on a person’s past behavior in order to build a model for the future. This will predict the future product buying, movie viewing or book reading by people. It also creates a filtering approach using the discrete characteristics of items while recommending additional items.

## Compare SAS, R and Python programming?

SAS: it is one of the most widely used analytics tools used by some of the biggest companies on earth. It has some of the best statistical functions, graphical user interface, but can come with a price tag and hence it cannot be readily adopted by smaller enterprises

R: The best part about R is that it is an Open Source tool and hence used generously by academia and the research community. It is a robust tool for statistical computation, graphical representation and reporting. Due to its open source nature it is always being updated with the latest features and then readily available to everybody.

Python: Python is a powerful open source programming language that is easy to learn, works well with most other tools and technologies. The best part about Python is that it has innumerable libraries and community created modules making it very robust. It has functions for statistical operation, model building and more.

## What is Linear Regression?

Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.

## What is Interpolation and Extrapolation?

Estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

## Explain the various benefits of R language?

The R programming language includes a set of software suite that is used for graphical representation, statistical computing, data manipulation and calculation.

Some of the highlights of R programming environment include the following:

- An extensive collection of tools for data analysis
- Operators for performing calculations on matrix and array
- Data analysis technique for graphical representation
- A highly developed yet simple and effective programming language
- It extensively supports machine learning applications
- It acts as a connecting link between various software, tools and datasets
- Create high quality reproducible analysis that is flexible and powerful
- Provides a robust package ecosystem for diverse needs
- It is useful when you have to solve a data-oriented problem

## What is power analysis?

An experimental design technique for determining the effect of a given sample size.

## What is Collaborative filtering?

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

## What are the two main components of the Hadoop Framework?

HDFS and YARN are basically the two major components of Hadoop framework.

**HDFS-**Stands for Hadoop Distributed File System. It is the distributed database working on top of Hadoop. It is capable of storing and retrieving bulk of datasets in no time.**YARN-**Stands for Yet Another Resource Negotiator. It allocates resources dynamically and handles the workloads.

## How do Data Scientists use Statistics?

Statistics helps Data Scientists to look into the data for patterns, hidden insights and convert Big Data into Big insights. It helps to get a better idea of what the customers are expecting. Data Scientists can learn about the consumer behavior, interest, engagement, retention and finally conversion all through the power of insightful statistics. It helps them to build powerful data models in order to validate certain inferences and predictions. All this can be converted into a powerful business proposition by giving users what they want at precisely when they want it.

## What is logistic regression?

It is a statistical technique or a model in order to analyze a dataset and predict the binary outcome. The outcome has to be a binary outcome that is either zero or one or a yes or no.

## Why data cleansing is important in data analysis?

With data coming in from multiple sources it is important to ensure that data is good enough for analysis. This is where data cleansing becomes extremely vital. Data cleansing extensively deals with the process of detecting and correcting of data records, ensuring that data is complete and accurate and the components of data that are irrelevant are deleted or modified as per the needs. This process can be deployed in concurrence with data wrangling or batch processing.

Once the data is cleaned it confirms with the rules of the data sets in the system. Data cleansing is an essential part of the data science because the data can be prone to error due to human negligence, corruption during transmission or storage among other things. Data cleansing takes a huge chunk of time and effort of a Data Scientist because of the multiple sources from which data emanates and the speed at which it comes.

## What is the difference between Cluster and Systematic Sampling?

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection, or cluster of elements. Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list,it is progressed from the top again. The best example for systematic sampling is equal probability method.

## Are expected value and mean value different?

They are not different but the terms are used in different contexts. Mean is generally referred when talking about a probability distribution or sample population whereas expected value is generally referred in a random variable context.

**For Sampling Data**

Mean value is the only value that comes from the sampling data.

Expected Value is the mean of all the means i.e. the value that is built from multiple samples. Expected value is the population mean.

**For Distributions**

Mean value and Expected value are same irrespective of the distribution, under the condition that the distribution is in the same population.

## Describe univariate, bivariate and multivariate analysis.

As the name suggests these are analysis methodologies having a single, double or multiple variables.

So a univariate analysis will have one variable and due to this there are no relationships, causes. The major aspect of the univariate analysis is to summarize the data and find the patterns within it to make actionable decisions.

A Bivariate analysis deals with the relationship between two sets of data. These sets of paired data come from related sources, or samples. There are various tools to analyze such data including the chi-squared tests and t-tests when the data are having a correlation. If the data can be quantified then it can analyzed using a graph plot or a scatterplot. The strength of the correlation between the two data sets will be tested in a Bivariate analysis.

## How machine learning is deployed in real world scenarios?

Here are some of the scenarios in which machine learning finds applications in real world:

- Ecommerce: Understanding the customer churn, deploying targeted advertising, remarketing
- Search engine: Ranking pages depending on the personal preferences of the searcher
- Finance: Evaluating investment opportunities & risks, detecting fraudulent transactions
- Medicare: Designing drugs depending on the patient’s history and needs
- Robotics: Machine learning for handling situations that are out of the ordinary
- Social media: Understanding relationships and recommending connections
- Extraction of information: framing questions for getting answers from databases over the web

## What does P-value signify about the statistical data?

P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

• P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.

• P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.

• P-value=0.05is the marginal value indicating it is possible to go either way.

## Do gradient descent methods always converge to same point?

No, they do not because in some cases it reaches a local minima or a local optima point. You don’t reach the global optima point. It depends on the data and starting conditions.

## What are the various aspects of a Machine Learning process?

In this post I will discuss the components involved in solving a problem using machine learning.

- Domain knowledge

This is the first step wherein we need to understand how to extract the various features from the data and learn more about the data that we are dealing with. It has got more to do with the type of domain that we are dealing with and familiarizing the system to learn more about it. - Feature Selection

This step has got more to do with the feature that we are selecting from the set of features that we have. Sometimes it happens that there are a lot of features and we have to make an intelligent decision regarding the type of feature that we want to select to go ahead with our machine learning endeavor. - Algorithm

This is a vital step since the algorithms that we choose will have a very major impact on the entire process of machine learning. You can choose between the linear and nonlinear algorithm. Some of the algorithms used are Support Vector Machines, Decision Trees, Naïve Bayes, K-Means Clustering, etc. - Training

This is the most important part of the machine learning technique and this is where it differs from the traditional programming. The training is done based on the data that we have and providing more real world experiences. With each consequent training step the machine gets better and smarter and able to take improved decisions. - Evaluation

In this step we actually evaluate the decisions taken by the machine in order to decide whether it is up to the mark or not. There are various metrics that are involved in this process and we have to closed deploy each of these to decide on the efficacy of the whole machine learning endeavor. - Optimization

This process involves improving the performance of the machine learning process using various optimization techniques. Optimization of machine learning is one of the most vital components wherein the performance of the algorithm is vastly improved. The best part of optimization techniques is that machine learning is not just a consumer of optimization techniques but it also provides new ideas for optimization too. - Testing

Here various tests are carried out and some these are unseen set of test cases. The data is partitioned into test and training set. There are various testing techniques like cross-validation in order to deal with multiple situations.

## What do you understand by the term Normal Distribution?

It is a set of continuous variable spread across a normal curve or in the shape of a bell curve. It can be considered as a continuous probability distribution and is useful in statistics. It is the most common distribution curve and it becomes very useful to analyze the variables and their relationships when we have the normal distribution curve.

The normal distribution curve is symmetrical. The non-normal distribution approaches the normal distribution as the size of the samples increases. It is also very easy to deploy the Central Limit Theorem. This method helps to make sense of data that is random by creating an order and interpreting the results using a bell-shaped graph.

## A test has a true positive rate of 100% and false positive rate of 5%. There is a population with a 1/1000 rate of having the condition the test identifies. Considering a positive test, what is the probability of having that condition?

Let’s suppose you are being tested for a disease, if you have the illness the test will end up saying you have the illness. However, if you don’t have the illness- 5% of the times the test will end up saying you have the illness and 95% of the times the test will give accurate result that you don’t have the illness. Thus there is a 5% error in case you do not have the illness.

Out of 1000 people, 1 person who has the disease will get true positive result.

Out of the remaining 999 people, 5% will also get true positive result.

Close to 50 people will get a true positive result for the disease.

This means that out of 1000 people, 51 people will be tested positive for the disease even though only one person has the illness. There is only a 2% probability of you having the disease even if your reports say that you have the disease.

## What is the difference between Supervised Learning an Unsupervised Learning?

If an algorithm learns something from the training data so that the knowledge can be applied to the test data, then it is referred to as Supervised Learning. Classification is an example for Supervised Learning. If the algorithm does not learn anything beforehand because there is no response variable or any training data, then it is referred to as unsupervised learning. Clustering is an example for unsupervised learning.

## What is Linear Regression?

It is the most commonly used method for predictive analytics. The Linear Regression method is used to describe relationship between a dependent variable and one or independent variable. The main task in the Linear Regression is the method of fitting a single line within a scatter plot. The Linear Regression consists of the following three methods:

- Determining and analyzing the correlation and direction of the data
- Deploying the estimation of the model
- Ensuring the usefulness and validity of the model

It is extensively used in scenarios where the cause effect model comes into play. For example you want to know the effect of a certain action in order to determine the various outcomes and extent of effect the cause has in determining the final outcome.

## What is K-means? How can you select K for K-means?

K-means clustering can be termed as the basic unsupervised learning algorithm. It is the method of classifying data using a certain set of clusters called as K clusters. It is deployed for grouping data in order to find similarity in the data.

It includes defining the K centers, one each in a cluster. The clusters are defined into K groups with K being predefined. The K points are selected at random as cluster centers. The objects are assigned to their nearest cluster center. The objects within a cluster are as closely related to one another as possible and differ as much as possible to the objects in other clusters. K-means clustering works very well for large sets of data.

## How is Data modeling different from Database design?

Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying the data modeling techniques.

Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. Strictly speaking database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.

## What is the goal of A/B Testing?

It is a statistical hypothesis testing for randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. An example for this could be identifying the click through rate for a banner ad.

## What is an Eigenvalue and Eigenvector?

Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching. Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

## What is the difference between data science and big data?

Data science is a field applicable to any data sizes. Big data refers to the large amount of data which cannot be analysed by traditional methods.

## Which would you prefer – R or Python?

Both R and Python have their own pros and cons. R is mainly used when the data analysis task requires standalone computing or analysis on individual servers. Python, when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database.

## How can outlier values be treated?

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for large number of outliers the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values.The most common ways to treat outlier values –

1) To change the value and bring in within a range

2) To just remove the value.

## How can you assess a good logistic model?

There are various methods to assess the results of a logistic regression analysis-

• Using Classification Matrix to look at the true negatives and false positives.

• Concordance that helps identify the ability of the logistic model to differentiate between the event happening and not happening.

• Lift helps assess the logistic model by comparing it with random selection

## What is selection bias, and how can you avoid it?

Selection bias is an experimental error that occurs when the participant pool, or the subsequent data, is not representative of the target population.

Selection biases cannot be overcome with statistical analysis of existing data alone, though Heckman correction may be used in special cases.

## Which package is used to do data import in R and Python? How do you do data import in SAS?

In R, RODBC is used for RDBMS data and data.table for fast import.

In SAS, data and sas7bdat is used to import data.

In Python, Pandas package and the commands read_csv, read_sql are used for reading data.

## What are various steps involved in an analytics project?

Understand the business problem

• Explore the data and become familiar with it.

• Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.

• After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.

• Validate the model using a new data set.

• Start implementing the model and track the result to analyse the performance of the model over the period of time.

## How can you iterate over a list and also retrieve element indices at the same time?

This can be done using the enumerate function which takes every element in a sequence just like in a list and adds its location just before it.

## Which technique is used to predict categorical responses?

The classification techniques is used to predict categorical responses.

## Explain what resampling methods are.

Resampling methods are used to estimate the precision of the sample statistics, exchanging label on data points and validating models.

## During analysis, how do you treat missing values?

The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored.There are various factors to be considered when answering this question-

- Understand the problem statement, understand the data and then give the answer.Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.
- If it is a categorical variable, the default value is assigned. The missing value is assigned a default value.
- If you have a distribution of data coming, for normal distribution give the mean value.
- Should we even treat missing values is another important point to consider? If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

## Name some of the prominent resampling methods in data science

The Bootstrap, Permutation Tests, Cross-validation and Jackknife

# How do data management procedures like missing data handling make selection bias worse?

Missing value treatment is one of the primary tasks which a data scientist is supposed to do before starting data analysis. There are multiple methods for missing value treatment. If not done properly, it could potentially result into selection bias. Let see few missing value treatment examples and their impact on selection-

**Complete Case Treatment:** Complete case treatment is when you remove entire row in data even if one value is missing. You could achieve a selection bias if your values are not missing at random and they have some pattern. Assume you are conducting a survey and few people didn’t specify their gender. Would you remove all those people? Can’t it tell a different story.

**Available case analysis: **Let say you are trying to calculate correlation matrix for data so you might remove the missing values from variables which are needed for that particular correlation coefficient. In this case your values will not be fully correct as they are coming from population sets.

**Mean Substitution: **In this method missing values are replaced with mean of other available values.This might make your distribution biased e.g., standard deviation, correlation and regression are mostly dependent on the mean value of variables.

Hence, various data management procedures might include selection bias in your data if not chosen correctly.

## What are the basic assumptions to be made for linear regression?

Normality of error distribution, statistical independence of errors, linearity and additivity.

## Can you write the formula to calculat R-square?

R-Square can be calculated using the below formular –

1 – (Residual Sum of Squares/ Total Sum of Squares)

## What is the advantage of performing dimensionality reduction before fitting an SVM?

Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.

## How will you assess the statistical significance of an insight whether it is a real insight or just by chance?

Statistical importance of an insight can be accessed using Hypothesis Testing.

## What is Machine Learning?

The simplest way to answer this question is – we give the data and equation to the machine. Ask the machine to look at the data and identify the coefficient values in an equation.

For example for the linear regression y=mx+c, we give the data for the variable x, y and the machine learns about the values of m and c from the data.

## You created a predictive model of a quantitative outcome variable using multiple regressions. What are the steps you would follow to validate the model?

Since the question asked, is about post model building exercise, we will assume that you have already tested for null hypothesis, multi collinearity and Standard error of coefficients.

Once you have built the model, you should check for following –

- Global F-test to see the significance of group of independent variables on dependent variable
- R^2
- Adjusted R^2
- RMSE, MAPE

In addition to above mentioned quantitative metrics you should also check for-

- Residual plot
- Assumptions of linear regression

## How can you deal with different types of seasonality in time series modelling?

Seasonality in time series occurs when time series shows a repeated pattern over time. E.g., stationary sales decreases during holiday season, air conditioner sales increases during the summers etc. are few examples of seasonality in a time series.

Seasonality makes your time series non-stationary because average value of the variables at different time periods. Differentiating a time series is generally known as the best method of removing seasonality from a time series. Seasonal differencing can be defined as a numerical difference between a particular value and a value with a periodic lag (i.e. 12, if monthly seasonality is present)

## Can you cite some examples where both false positive and false negatives are equally important?

In the banking industry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses.

Banks don’t want to lose good customers and at the same point of time they don’t want to acquire bad customers. In this scenario both the false positives and false negatives be**come very important to measure.**

These days we hear many cases of players using steroids during sport competitions Every player has to go through a steroid test before the game starts. A false positive can ruin the career of a Great sportsman and a false negative can make the game unfair.

## Can you explain the difference between a Test Set and a Validation Set?

Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid Overfitting of the model being built. On the other hand, test set is used for testing or evaluating the performance of a trained machine leaning model.

In simple terms ,the differences can be summarized as-

- Training Set is to fit the parameters i.e. weights.
- Test Set is to assess the performance of the model i.e. evaluating the predictive power and generalization.
- Validation set is to tune the parameters.

## What do you understand by statistical power of sensitivity and how do you calculate it?

Calculation of seasonality is pretty straight forward-

** Seasonality = True Positives /Positives in Actual Dependent Variable**

Where, True positives are Positive events which are correctly classified as Positives.