What is data science

0:06 Hello everyone and welcome to the data science and AI full course. Your complete guide to mastering datadriven intelligence. In today's world, data is everywhere and artificial intelligence is transforming how we use it. From predicting trends and building recommendation systems to creating intelligent chat bots and self-draining technologies, data science and AI are at the core of innovation. In this course, you will start with the foundations of data science and then move into machine learning, deep learning and AI techniques.

0:29 You will also explore real world projects that brings these concepts to life. And by the end of this course, you will have both the theoretical knowledge and practical experience to apply data science and AI in solving real world problems and advancing your career in this exciting field. So before we begin, please like, share and subscribe to Edureka's YouTube channel and hit the bell icon to stay updated on the latest content from Edureka. Also check out Edureka's artificial intelligence course.

0:53 AI is transforming industries by streamlining processes and driving smarter decision making. With Edureka's AI certification courses, you will gain hands-on experience in data science and AI, machine learning, deep learning, NLP, and generative AI. Master powerful tools like Python, TensorFlow, PyTorch, and Open AI to build intelligent systems. Whether you're bringing your journey or enhancing your skills, these courses will equip you for a high demand careers in an AI powered world. So check out the course link given in the description box below.

1:40 So first let us get started by understanding what data science is. What is data science? With the development of new technologies, there has been a rapid increase in the amount of data. This has created an opportunity to analyze and derive meaningful information from all this data. This is where data science comes into the picture. Technically, data science is defined as the process of extracting knowledge and insights from complex and large sets of data by using processes like data cleaning, data visualization.

2:05 Almost all of us use Google Maps. But have you ever wondered how Google knows the traffic conditions between where you are and where you're trying to go? Or how does it determine the fastest way to your destination? The answer is data science. Google Maps collects data every day from a multitude of reliable sources that primarily includes smartphones. It continuously combines the data from drivers, passengers, and pedestrians. and then making use of machine learning algorithms, Google Maps sends real traffic updates by way of colored lines on the traffic layers.

2:51 This helps you find your optimal route and even determine which areas should be avoided due to road work or accidents. Isn't that amazing? As data science continues to evolve, the demand for skilled professionals in this domain is also increasing drastically. In order to uncover useful intelligence for their organizations, data scientists must master all the aspects of data science. This is a major reason why data science enthusiasts are rushing to get certified. So the first reason why you should learn data science is because of the impressive salaries.

3:37 Data science is an area where there is a lot of high demand for professionals but a low supply of skilled labor. So anyone remotely good in the subject area can expect an impressive salary. And let me remind you that data science pays way better than traditional IT careers. As of making this video, there's an average salary of 10.5 lakhs peranom which can go as high as 26 lakhs perom for skilled and experienced professionals. Now, the second reason why you should opt for a career in data science is because of the great career opportunities.

4:01 Data science has consistently been one of the best career options for a while now. As I said before, there is a lot of demand for skilled and experienced professionals, but there is low supply of them. That is why if you get yourself trained and become comfortable with handling data, you can expect incredible opportunities. You should know that there are thousands of vacancies opening up every day and thousands of placements are taking place. So who says that you shouldn't be one of them?

4:25 Third reason is multiple positions. Data science has a lot of different tasks and people are recruited to work on particular subject areas. If you generalize these areas then you can come up with three different position. The first one is an analyst. The analyst works with tasks like gathering data, cleaning and organizing data, making insights, visualizing information, and much more. The second type is an engineer. The main focus of the specialization is to transform the raw data into something viable and readable before it is presented to the organization.

5:12 But it is not just this. They are also required to design, build, test, blend, manage and optimize data with the help of numerous sources. The last one are the scientists. Most of the time here goes for researching, writing algorithms and writing code to answer questions about the data sets in question. Now, the fourth reason why you should learn data science is because you get to work for the best organizations. Yes, data science is one of the fastest growing domains and with it comes a lot of opportunities to work for your dream company.

5:36 You can find opportunities in companies like Meta, Amazon, Netflix, Google, etc. all the way up to some super cool companies in startups. And need I tell you the fifth reason is less competition. Well, how much better can it get? The reason why I say less competition is because data science is relatively a new domain. So while there are a lot of opportunities, there just isn't enough people. Now you can pretty much guess the next point based on this.

5:59 It is quick growth. You see, data science is not like a traditional IT job. The opportunities bought by data science is applicable from things like market research and logistics to water supply, telecommunications and even restaurants. Since there is less competition, you can go to any field and manage a lot of tasks which result in a quick growth in your career and need to add the ever growing amount of data to this list. The seventh reason we have is that data scientists are decision makers.

6:46 As a data science professional, you will often find yourself at the center of decision-making activities and you will be in charge of making decisions that will change how a business grows. You'll play an integral role in how your business develops. Our eighth reason is also an extension of this. All that decision making will lead to business optimization. You may develop the necessary skills to support the expansion of your business. You can be an employee or an entrepreneur.

7:10 Data science can help you optimize the process efficiency and quality of your business and its offerings be it products or services. The ninth reason is that it is an easy subject to learn online. Now I promise you this is not an ad thing is systematic way of learning data science is super interactive and fulfilling. You can take up an online course and study it at your own pace whenever you are free. There are a lot of options to study this be it in edorica or other platforms.

7:33 All you have to do is Google it. The last thing that we must and should cover in this list is data science is a future oriented subject. Data is becoming an integral part of decision making in the 21st century and if you learn AI, ML, DL, CV, big data etc. you're preparing yourself for all these opportunities that are coming your way. While these opportunities are certainly enticing enough, reasons to learn data science is not limited to this.

7:59 If you want to learn data science or just want to familiarize yourself with its concepts, then you should definitely visit our channel. We have a lot of videos covering various topics in this subject. So, I guess I'll see you over there. What is web scraping? The simplest way to describe web scraping is by thinking of it as a technique that makes extraction and manipulation of data stored on some website possible. One simple example of this would be something like extract the prices of INR against USD and save that price in your variable.

8:45 The potential use cases for this technique is virtually limitless as it always depends on case to case basis. whatever the requirements of the problem statement may be. So how does web scraping work? First understand that web scraping is not a program itself. It is a code written in any programming language of choice like Java or Python. The program intends to make a connection with the target website with whom the program will be interacting and then downloads the page.

9:09 After that, the program will take control of some elements and interact with them to achieve a certain task. Whether it may be to extract that element's value or click on specific button on the website or anything else. This is usually done by interacting with HTML elements and locators. From here on, it depends on the problem statement about what needs to be done. You can store data and save it on your local drive or send it to your email account using other functions and so on.

9:57 You can also create robots or save data or manipulate as the problem statement says. Now let's look at the tools or libraries required for web scraping. The first is probably the most important one and that is Selenium. Selenium is a project that essentially automates browsers. That's it. Whatever you do with that power is entirely up to the problem statement. Whether your requirement is to automate browser to capture data or feed data or any kind of data manipulation requirements you may have, Selenium has got everything.

10:22 Next, pandas. Pandas is a library for Python that provides powerful data analysis and manipulation capabilities. Just for example, if you have a table on some website, you could manipulate that table by using pandas and you could control the rows, columns and any field using pandas and later even export that table in a raw CSV format. And how can we forget beautiful soup? Beautiful soup is a Python library for pulling data from HTML and XML files. It makes it easy to parse information from web pages.

11:10 There are some differences between beautiful soup and selenium. So always remember this key comparison when working on projects. Beautiful soup is best suited for small project whereas selenium is ideal for complex projects. And lastly, I would like to repeat that web scraping is a technique and it is not limited to a single programming language. I personally developed many project on web scraping using Python but others have done that using UiPath and also Java. Some developers prefer PHP for building their web automation project.

11:35 So it really depends on the developers to choose languages as they see fit. And now let's talk about some legal complications related to the art of web scraping. First, it may not be allowed by some websites that you scrape their data. So, it is ideal to refer to the terms of the website and fair usage policies before you start scraping the website. Also, if you don't find the clauses that you need in the website terms and conditions, it would be a good idea to ask the website owner if you can scrape their website.

12:24 However, in most cases, web scraping is all right. As long as you are experimenting with the website and learning to scrape data for your own projects or learning purposes, it would be okay. However, if you intend to sell the data from the target website or make it accessible to public for free, then it might become a problem for you as you may be legally doing something that isn't right. So, when in doubt, remember web scraping is okay for personal usage, not commercial.

12:48 Let us understand what exactly predictive analysis is. So what is predictive analysis? Predictive analytics or analysis encompasses a variety of statistical techniques from data mining, predictive modeling and machine learning that actually analyze the current and historical facts to make predictions about future or otherwise unknown events. So this is the basic definition from Wikipedia. So we basically use the previously collected data to predict an outcome or an event. So typically historical data is used to build a mathematical model.

13:37 In our case we can call it a classifier or a predictive model or a regressor which actually captures the important trends and then the current data is used on that model to predict what will happen next or to suggest actions to take optimal outcomes. So let us take a look at various applications where we can actually use predictive analysis. So we can use predictive analysis for lot of things. First of all, we have campaign management. So let's say we have a campaign.

14:00 We have to figure out what kind of audience will be there or what kind of our target audience is. So we can analyze the previous data of our previous campaigns that we might have managed previously and according to that we can figure out some suggestions or you know the course of action that we have to take. So this is one campaign management that we can do using the predictive analysis or for recent examples let's say an election campaign you know lot of people are gathering a lot of data of previous elections like how it happened and what are the major factors that led to the winning of some so and so person.

14:45 So this is how we can use predictive analysis and campaign management. Then there is customer acquisition. So we can analyze the whole business and we can figure out different points to you know figure out what kind of task or events we can actually produce in order to make our business better so that we'll be able to make customer acquisition better and then we have budgeting and forecasting as well. Similarly, you know, taking a look at previous data, we can finalize some budget and forecast few uh related pointers.

15:08 For example, we have stock prediction using Python or any other language such as R also. And then we have fraud detection. So, we can you know manage a lot of data like for credit card companies. They make use of hundreds and hundreds of users and they analyze the data to predict or you know detect the fraudulent transactions in their data. And then there is promotions as well. So we can analyze you know the target audience. We can uh follow the trends like they are following you know the types of content they are actually going for and then similarly you can make promotions according to that.

15:31 Then there's pricing also like you can figure out let's say you have a supermarket somewhat like you know what Walmart does. So you have all the pricing and everything. So what you can do is figure out the price of a product after several time based on the recent purchases and also the recent scenario or the previous historical data upon which the price has been distributed accordingly and you can also plan for the demand as well using the predictive analysis.

15:53 So these are a few applications that I can think of right now and these are only a few applications where you can use predictive analysis to predict the for example I'll talk about football guys. So let's say if you have a favorite player and in the next season you want to see how much price he might go for in certain other clubs. So you can make use of the data at your bay and depending on the purchases that happened in previous seasons or the windows you can actually figure out somewhat around what kind of price your favorite player is going to go for.

16:40 So that is one example I can think of right now. So these are a few applications of predictive analysis. Now let's move on to the next topic of the session guys which is steps involved in predictive analysis. So this is a very important concept in this session guys. So you have to fully understand what kind of steps that goes in while you're doing a predictive analysis. So the first step has to be a data exploration. So what you have to do is gather data uploaded into your program.

17:03 Then you have to take a look at your data in a perspective which will clear certain things for you. Like you have to figure out what kind of data you're dealing with. What are the columns? What are the features that you have inside your data? What kind of data it is? How many numerical values are there? What kind of data types are there inside your data? Is it a CSV file or not? So on so you have to figure out a lot of things while data exploration.

17:26 And after that you can uh figure out how to clean your data. By cleaning I mean you have to figure out the redundancies that might hinder your model. So for that you have to check for null values. You have to check for missing values and then you have to figure out what kind of columns will be actually better if you put them inside your model and what are the redundant variables like what kind of columns that you can actually remove and will not make a difference in your model.

17:49 So that covers the data cleaning part and then there is modeling where you have to model your or you have to select your predictive model guys. So there are a lot of models that you can go for but in this session I'm going to use the linear regression model because it's the very simple or the basic one so that the beginners also will be able to learn it properly. After modeling you have to check for the evaluation or you have to check for the accuracy you know how your model is actually performing.

18:10 So let's talk about these steps in a little more detailed way. So we'll talk about data exploration first of all. As I've already told you, data exploration is gathering your data and then taking a look at your data in a perspective that will clear a lot of things. For example, you'll be able to see the number of columns, number of rows. You will have a description of all the data types, what kind of variables are there.

18:33 You will have the mean values, the average values, minimum values and uh you can also check for unique values in your columns as well. So, so this all comes in data exploration. And after this the second step is data cleaning. And I've already told you guys data cleaning is basically getting rid of redundancies in your data which includes the missing values which may hinder. And you have to make sure that your model is not going to cause overfitting or underfitting due to the noise and noise is basically irrelevant data that may be in the form of null values.

18:58 So you have to make sure you get rid of them or replace them with average values in the column. And then there is redundancies like outliers which are not necessarily required in your model. So you can remove them as well. So this is all about data cleaning. And then we have the third step which is modeling. So for data modeling first of all you have to understand the relationship between the variables in your model so that you'll figure out what kind of model you're going to go for.

19:21 So for example if let's say if you have a target variable in our case which will be a price of certain goods. So let's say you have to figure out the relationship between variables. So if you're going for linear regression, you have to make sure the relationship is continuous. And let's say if you are going for logistic regression, it is important that you go for continuous variables. The target variable although has to be dichomous or what do you call it categorical which is like let's say if I'm trying to predict something using the logistic regression the answer would probably be yes or no or be one or zero something like that but in case of linear regression we have to make sure that there is some continuous relationship between the variables which is my target variable and my independent variables taking a look at the fourth part or the fourth step and the final step is performance analysis.

20:31 So after you're done making a model, so you have to perform certain analysis which is you know checking the accuracy of the model and making sure that it's above 70. I mean if you are a beginner and if you are trying to make your first prediction model anything above 70% accuracy score is very good guys but I would suggest that if you are working on a good model and if you want your model to be good the accuracy should be ranging around 0.9 which is more than 90% and if you get it the first time it's well and good but it solely depends on your data and the kind of model selection that you do.

20:55 So let's take a look at the next topic in our session guys. So this is basically where I'm going to perform predictive analysis using Python on a data set. So I have a problem statement in which I have a data set which has certain values or certain variables which has columns like you know how many bedrooms does a house have and what kind of square feet it is grabbing and all these things that I'll show you in the data and using that data I am going to predict the house of a price.

21:19 So let's take it up to Jupyter notebook guys and I'll show you what I'm going to do over there. So I have a Jupyter notebook over here guys and if you're not familiar with Jupyter notebook guys, I suggest you to check out our tutorial on YouTube. We have a Jupyter notebook tutorial. You'll be able to learn it properly. I mean there's not really so much to learn in Jupyter notebook. It's quite easy. That is why I'm using also.

21:41 And we have a cheat sheet as well. So you can go for that. So first of all I'm going to import some dependencies. So for the first step that is data exploration I have to get the data. So for that I'm going to use the pandas library and I'll import a few other libraries as well like I'm going to use the seaborn to check the relationship between the variables basically for EDA exploratory data analysis and if you guys don't know what EDA is I suggest you to check out another tutorial which is exploratory data analysis that we have on our YouTube channel.

22:27 And then I'm going to import numpy as well just in case. All right. And you can see guys I have to just press shift and enter. And uh this is why I'm using uh Jupyter notebook because the implementation is very easy and I can segregate my uh data or the code in different cells. So I'm importing this u and I can just make it okay I'll make it a little bigger so that it's visible to you everyone.

22:52 What I can do is I can comment a part. Let's say installing dependencies and it is in a separate cell. So that makes it quite descriptive when you're coding and when you are trying to figure out what's wrong in your code. It helps actually. So after this what we have to do is I'll check I'll have to import the data. For that I'm going to use the read CSV module which is basically going to go to the file and read my data guys.

23:16 The name of the file is housecsv. We have a truncated error. All right. So guys I have to show you something. So usually when you do this when you copy the file location you get that uh uni code error. But let's see if I change these back slashes to forward back slashes. What happens? Do I still get the error or not? Okay, we have uh all right. So I was doing something wrong over here. So this is one exercise for you guys.

23:39 Like earlier when I was using the backward backslashes, I was getting a uni code error. But when I change it to the forward backslashes, I'm not getting that error. So this is one question for you guys. Tell me why you think it happened in the comment sections below. Now moving on, I'll take the first look at my data guys. So I'll just use the head method to get my first data. So these are the first five rows in my data guys.

24:24 So we I have ID, we have date, the price is there, bedrooms, we have bathrooms, square ft living and square ft lot, we have floors as well. Waterfront is zero. Okay, it has to be zero and one I guess there is view then grade square above. So these are the columns that I have inside my data guys. So I'll check the last five rows as well. For that I am using the tail method. So as you can see you can get the first look of your data using the data dot head and data.tail method.

24:47 After that I check the columns of my data. And let's check the shape as well guys so that we'll know what we're dealing with. Okay it's not callable. All right. So we have 21,613 entries with 21 columns. Okay. It's a quite big data set and let me tell you guys this is one data set that I found on kegle and it's very easy to find the house data set and I'm using this example of house data set because it's very common and to find this data set is very easy you go on to kegle and you just look for house prediction uh data sets and it will show you a lot of data sets that you can download there from okay so you can find the data set on kegle guys now we have checked the shape as well okay I'll use one more method that is data So describe all right this callable.

25:34 All right. So we have all these numerical values and using the describe method we can get the 50% minimum, maximum and the standard deviation. We can get the mean value and the count as well. So let's say for bedrooms the mean value is three. Uh the most common entry in the bedroom section is a threebedroom house and then for bathrooms also is a twob house. square ft² is almost 2079 square ft² and then for maximum values we have even a 33bedroom house as well and we have a house with eight bathrooms and the square feet is 13,540 all right so minimum value is we have a zero bedroomedroom house okay that's going to be something else and square ft 290 so this this is how you use the describe method and this is the first step guys I'm trying to do the data exploration now after this I think I'm pretty sure about what kind of data I'm

26:45 dealing with. Now what I'm going to do I'm going to move on to the next step that is checking for the relationship between these variables. So for that I'm going to use the data visualization and I'm going to use a few I'm going to use a few plot points using the seabon library. And if you don't know about seaborn we have a YouTube uh tutorial on seaborn library as well. So you can find out different kinds of uh plots that you you can use for data visualization and data visualization is nothing but it's a process where you can visualize your data and you can try to figure out the relationship between the variables.

27:10 Before that I want to check for null values or missing values because I don't want to get any hindrance in my data set where I'm modeling. So first of all you have to do check for null values and let's get a sum as well. So we have zero almost. Okay. So we have no null values in this data set. So usually if you find a null value and if it's a big data set and let's say if all these values are let's say 2,000 and if you have 10 missing values you can just remove those 10 values.

27:33 But if there are more null values I suggest you to replace them with the mean value. And to find out the mean value, you can just go over here. Let's say if you have uh let's say if you're checking for bedrooms, how many null values are there? And let's say if there are 500 out of 21,000. So you can just uh replace the zero with this value which is three. And similarly for any other column, you can do the same using the mean value.

27:58 So since there are no null values inside this data set because it's a very uh clean data set that I downloaded it from kegle. So we're going to move on to the next step which is visualization. And mind you guys, this is the step in my data exploration part and the data cleaning part, not the other step that we use for predictive analysis. All right, so I have no null values, but there are a few redundancies that I want to get rid of.

28:20 I'll talk about that later, guys. First of all, I'm going to use a relation plot X. I'll use Okay. So, I want to check or my basic aim is to predict the price of the house. All right. So for X I'll just take price and let's check the relationship between uh these variables. So I'll check for bedroom. All right. Kind is equal to Okay. We'll not use kind data is equal to data. Okay. All right. So I think we can like almost there are so many I mean so this is one uh relationship that I'm getting over here.

29:33 price is not very clear but we are getting the relationship of the bedrooms or the most common bedrooms. So which is around over here that is 0 to 5 and similarly I can check for other variables as well like bathrooms see guys so the price is actually increasing with the number of bathrooms but it's not necessarily um same for everything so there has to be some other dependencies as well because as you can see since the bathrooms are pricing price is not actually rising that much okay I'll just copy this for bedrooms the price is actually increasing uh pretty much with each bedroom.

30:24 I mean not really uh if we take a look at 10 bedrooms also the price is pretty much the same. So there's not one decisive factor I can think of for this. All right so we'll check for some other as well. So we'll check for square square feet living. So this is one uh linear relationship that I'm seeing over here guys. So with each uh square ft rising most of the prices actually in this area only like from 0 to 40 400,000 or 40 million actually but uh we can see that it's a linear relationship with each increasing um square feet the price is actually rising.

31:13 So this is one thing that has to be there in inside our train set. I'll tell you what a train set actually is. Then there is floor as well. We can check for floors. All right. Okay. So, floors is actually uh pretty descriptive over here. So, for most of the values are in the two floors area and then we can check for waterfront as well. Okay. We can do one thing. I'll tell you one uh trick.

31:36 So we'll add the hue over here and this is going to be water front. So with the houses uh which actually has a waterfront are in oranges and the other ones are in blue. So you can see the relationship between them and uh similarly I can use other. Okay. So let's say latitude and longitude. So you can figure out the relationship between the variables using uh the visualization. So for me I think in this data set to get the price out we're going to have to use bedrooms bathroom there has to be square feet a lot of square feet has to be there floor also we can get and then what view front view we'll use square feet we have to use and build and year renovated we can leave it out from the train set and zip code also we don't actually need latitude and longitude are also not decisive in uh prediction because we can use it for uh visualization and we can actually get the picture over there and square feet living is actually important.

32:54 So these are the redundancies that I was talking about inside your model. So now we'll move on to the modeling part guys. So what I'll do now is I'll import a few dependencies guys. So from skarn. So first of all I have to import the linear regression from linear models and I'm going to import linear regression. Right. So I'll import the model selection import data split. All right. So, first thing that I have to do is I have to segregate my data into a training set and a test set.

33:48 So, I'll do one thing guys. Right. So, I'll get my data in this cell. So, two things that I don't actually need inside my data are date because u I'll get the training data or I'll write it as train. So which is going to be data and I'm going to drop a few columns. So I'll have to drop So I have to drop a few columns. So first of all I have to drop price because it cannot be in my training set since I'm going to predict it.

34:11 And then there has to be ID. We don't actually need it. And then I'm going to drop date as well. There's not really prove a lot of things over here. So for now I'll just remove all these columns. And uh for our uh test set or the dependent variable let's say I'm going to take the variable as test let's say and for this I'm going to use data dot or we need actually just one column that is price right method object is not subscriptible I'm sorry I made a syntax error over here guys I have to add the axis as well now it's fine guys so now what I do is I'll use I'm going to segregate my data into X train X test Y train and Y test and now I'm going to use the train test split method so first we have train then we have test we have test size which is let's say 0.3 three and we have the random

35:43 state is equal to let's say two right so we have made our x and x method now I'll use one variable let's say regr regressor and now I'm going to call my linear regression model it's made guys so now I'm going to use the fit method to fit my x train and x y train data guys the training data I have to fit Right, we have no errors here, guys. After this, I can just uh Okay, I'll take one variable.

36:07 Let's say predict and gr dot predict x test and y test. All right, wait a second, guys. I'm sorry. So now the modeling part is done guys. So I'll explain what I've done over here. I took the linear model which is linear regression. And then for segregating my data into training and testing set I'm using the train test split. Before that I segregated the data for my model inside which I'm using the training set which has all the values from the data set except price, ID and date.

36:57 So I've removed these three columns because I thought these are redundant for my model right now. And the variable that I'm going to predict over here is uh the price. So I'm taking that alone. So after that I use a train test split method to actually separate the data into training and test set and then I call the linear regression model over here using the regression model. I'm fitting the training data and after that I am using it to predict a value.

37:20 So now comes the part where we have to check the efficiency of the model. So for regression models it is very easy guys. You can just uh check the score for this. You have to provide a few values X test and Y test. And we have the accuracy of 0.70 which is not bad guys if you're using the model or the data set this big. It is quite uh predictable get this kind of accuracy. But you can do something else to improve the accuracy.

37:45 I mean uh you can look at the data and remove all the values that you find will help you into improving your accuracy like you can remove latitude, longitude, you can remove zip code, year renovated, your built that you can uh actually remove waterfront and view as well or keep only a few values that you actually need which is bedrooms, bathrooms I think and everything related to square feet that if you keep in your training set then it's going to be a higher accuracy for you guys.

38:07 All right. So now that we are done with the session guys, I want to give you a exercise sort of which you can do for practicing this. So I have used the linear regression model over here. So I want you to do one thing guys. Check out other classifiers and regressor models that you can use to predict a value. I mean we have a tutorial on all of them on our YouTube channel and see if you can use the same data to make a prediction model using other classifiers like a random forest classifier.

38:53 Then we have a decision tree. then you can use the logistic regression for this as well. I mean if you have continuous data then you can go for linear regression but if you find a categorical data let's say we have waterfront or not. So you can check for that as well. As you know we are living in a world of humans and machines. The humans have been evolving and learning from the past experience since millions of years.

39:16 On the other hand, the era of machines and robots have just begun. In today's world, these machines or the robots are like they need to be programmed before they actually follow your instructions. But what if the machines started to learn on their own? And this is where machine learning comes into picture. Machine learning is the core of many futuristic technological advancement in our world. Today you can see various examples or implementation of machine learning around us such as Tesla's self-driving car, Apple Siri, Sophia AI robot and many more are there.

39:39 So what exactly is machine learning? Well, machine learning is a sub field of artificial intelligence that focuses on the design of system that can learn from and make decisions and predictions based on the experience which is data. In the case of machines, machine learning enables computer to act and make datadriven decisions rather than being explicitly programmed to carry out a certain task. These programs are designed to learn and improve over time when exposed to new data.

40:03 Let's move on and discuss one of the biggest confusion of the people in the world. They think