Machine Learning Tutorial | Machine Learning Algorithm | Machine Learning Engineer Program | Edureka

Key Takeaways

so hello and welcome back to our regular session of machine learning training using python this is Atul from edureka and today our topic of discussion will be machine learning tutorial so without delaying any further let's drill down to today's agenda so our very first topic of this session will be why machine learning where I'll tell you how powerful the machine learning is and how various popular companies are implementing them next we'll drill down to see what exactly machine learning is and how does it work now once you understand how machine Learning Works in general then we'll move ahead and see what are the various types of machine learning and discuss how each of them work and how we are using them knowingly or unknowingly in our day-to-day life after we are done with the types of machine learning I'll tell you what you can do with machine learning once you know it's scope you might get a common question like which algorithm should I use in a specific situation or things like that so these things will be covered under this section finally in the last part we'll create six different machine learning models compare them and pick the best model and build the confidence such that the accuracy is most reliable and yes we'll be creating this using python I hope the agenda is clear to you guys so why machine learning to answer this let's see why most popular companies are using machine learning so starting with Netflix why Netflix is using machine learning I'm sure every one of you have used Netflix or at least heard about it right so do you know when you visit Netflix how do you get the list of movies which are very related to your interest so how does Netflix does that Netflix uses different algorithm that can predict and this recommend the user for the new content based on their previous watch History so the Netflix is using machine learning to generate a recommended list of movies for you so for instance say that I'm a Netflix user and have version series of movies like A B C D and E so that's my history now in Netflix mindsets data thereby finding Association rule it finds that the movies a b and F have been watched along with movies f and g by millions of user fine so there's a Association rule that Netflix uses recommend to its user with just like me have watched movies a b and F suppose you have watch movie A B and F so the Netflix will recommend movie G for you fine even the research suggests that more than 75 percent of what you are watching is being recommended by Netflix correct let's move ahead so next is the Facebook tag Facebook is using machine learning to Target in posts and images it automatically recognizes the face in the image and gives suggestion for who is the person in the image machine identifying the person in the picture really cool isn't it the next come one of the most trending and popular Innovation from Amazon the Alexa so Alexa is Amazon's virtual personal assistant that has been around for about two years and it keeps on getting smarter so Alexa is using a huge amount of machine learning starting from speech recognization to wake word detection for answering the question and even to the knowledge extraction and synthesis of spoken language a lot of computation is happening within the AWS so if you're a developer you can get the Alexa developer kit to know more about it find rest of you can just go ahead and purchase it from Amazon okay so the next and most common of them all is Spam filtering do you know how your mail is getting classified and filtered as spam or not a Spam meal you know what this is also done using the algorithms of machine learning fine I guess by now all of you are excited to know what machine learning is so without dealing any further let me tell you what is machine learning well Machine learning is a subset of artificial intelligence which provides systems or machine the ability to learn automatically and improve from experience without being explicitly programmed machine learning enables the computer to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task now these programs are designed to learn and improve over time when exposed to new data so every one of you got what does machine learning okay so let's move ahead and see in general how a machine learning work so one of the approaches is where the machine learning algorithm is trained using a labeled or unlabeled training data set to produce a model now this new input data is introduced into machine learning algorithm and it makes its prediction based on the model the prediction is evaluated for accuracy and if the accuracy is acceptable the machine learning algorithm is deployed and if the accuracy is not acceptable the machine learning algorithm is trained again and again with an augmented training data set fine so this is just a very high level example as there are many other factors and steps which are involved in a working of a machine learning fine so let's move on and sub-categorize machine learning into three different types first being the supervised learning next unsupervised learning and reinforcement learning let's see what each of them are how they work and how each of them is used in different fields don't worry I'll make sure that I use enough implementation of all three of them to give you a proper understanding so starting with supervised learning so what it is let's see the mathematical definition of supervised learning if you ask me mathematically supervised learning is where you have an input variable X and an output variable Y and you use an algorithm to learn the mapping function from input to the output such that y equal FX now your goal is to approximate the mapping function so well that when you have a new input data X so you can predict the output variable y for that data any question anyone okay Shivam has written that he didn't understood the concept of supervised learning okay Shivam let me simplify this we can rephrase or understand the mathematical definition of supervised learning as the machine learning method where each instances of training data set is composed of input attributes and unexpected output the input attribute of a training data set can be of any kind of data it can be a pixel of image a value of database row or it can even be a audio frequency histogram right so for each input instance an expected output value is associated with it this value can be a discrete representing a category or can be a real continuous value in either case the algorithm learns from the input pattern that generate the expected output now once the algorithm is trained it can be used to predict the correct output of a never seen input okay shubham is it clear now all right cool so let's move ahead this image shows an example of supervised learning process used to produce a model which is capable of recognizing ducts in the image the training data set is composed of label picture of ducts and non-ducks the result of supervised learning process is a predictive model which is capable of associating a labeled duct but not duck to a new image presented to the model now once it is trained the resulting predictive model can be deployed to a production environment a mobile app for example in order to recognize the new pictures fine now you might be wondering why this category of machine learning is named as supervised learning well it is called as supervised learning because the process of an algorithm learning from the training data set can be thought of as a teacher supervising the learning process we know the correct answer the algorithm iteratively makes while predicting on the training data set and is corrected by the teacher the learning stops when the algorithm achieves an acceptable level of performance fine now let's see some of the popular use cases of supervised learning starting with document classifier the machine or the model is trained with training data set one strain the machine can easily classify and sort the documents based on some criteria fine next is the weather app based on some prior knowledge like when it is sunny the temperature is higher or when it is cloudy the humidity is higher or Etc something like that so what the weather app is doing that based on some prior knowledge it is predicting the parameter for a given time another example is biometric attendance or biometric verification where you train the machine or a model and after a couple of input of your biometric identity media thumb Iris or earlobe the machine can validate your future input and can easily identify you okay so this was all about the supervised learning part now let's move ahead and learn about the next category of machine learning the unsupervised part so again mathematically unsupervised learning is where you have an input data X but there is no corresponding output variables Associated to it the goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data so before you ask me let me rephrase you this in a simple term an unsupervised learning approach the data instances of a training data set do not have an expected output value Associated to them instead the unsupervised learning algorithm detects pattern based on some inner characteristics of the input data an example of machine learning task that applies unsupervised learning is clustering in this task similar data instances are grouped together in order to identify clusters of the data you can see from the imager it shows an example of an unsupervised learning process this algorithm processes an unlabeled training data set and based on some instances or characteristics it grips the picture into three different clusters but despite the ability of the algorithm to group similar data into different cluster it cannot identify labels to the group The algorithm only knows which data instances are similar but it cannot identify the meaning of this group okay now again you might be wondering why this category of machine learning is named as unsupervised learning okay so these are called as unsupervised learning because unlike supervised learning there is no correct answer and there is no teacher algorithms are left on their own to discover and present the interesting structure in the data so let's see some of the example of unsupervised learning suppose your friend invites you to his party where you meet totally stranger now you classify them using the unsupervised learning that is you don't have any prior knowledge about them and this classification can be done on the basis of gender age group dressing educational qualification or whatever way you might like hold on guys we have a question from sashan okay how this learning is different from supervised learning well Shashank it's because you didn't add any prior or past knowledge about the people you just kept on classifying them on the go suppose you met person a and said that he belongs to X category person B he belongs to Y category person C he belongs to X category so what you are doing here you are just classifying them on the go you're meeting them and you're just classifying it okay is it clear now sashank okay fine so let's see another example suppose you have never seen a football match before and by chance you watch a video on internet now what you do you can classify the players on the basis of different Criterion like player wearing same sort of kits are in one class or player of one style are in one class which includes the players goalkeeper referee or even you can group the players on the base of the playing style like he's a attacker or he's a defender or whatever Way You observe thing you can just classify it okay so next is the popular recommendation system remember when you view products to purchase at Amazon it automatically recommends some other items to along with it so how does it do that well it's also an application of unsupervised learning the machine learns from the choice based on different user that is what is the most common product that a user is buying or viewing after purchasing or viewing the first product don't you think it's a really cool concept right well I personally found this feature really amazing so in the above example what you're doing is you're just creating the cluster based on some random criteria well even unsupervised learning use this same concept I assume by now you guys have a proper idea of what unsupervised learning means if you have any slightest doubt don't hesitate and add your doubt in the comments section I'll be happy to answer them for you okay let's discuss the third and the last type of the machine learning that is reinforcement learning so what is reinforcement learning reinforcement learning is a type of machine learning algorithm which allows this software agent and machine to automatically determine the idle Behavior within a specific context within a specific context to maximize its performance all it does is it performs hit and trial method to learn fine the reinforcement learning model is about the interaction between two elements the environment and the learning agent the learning agent leverages two mechanism namely exploration and exploitation when the learning agent acts on trial and error basis it is termed as exploration and when it is based on the knowledge gained from the environment it is termed as exploitation now the environment rewards the agent for correct action which is the reinforcement signal leveraging the rewards obtained the agent improves its environment knowledge to select the next action so in this example you can see that we have an agent which is confused to decide which among the two picture is of a duck so what it does it observes the environment and perform some action using some policies now once the action is performed the model gets a reward or penalty point for it as you can see that the machine has chosen bunny as a duck so it will get minus 50 point for the wrong answer okay now after it gets the penalty point the machine realizes that what path it followed or what decision it made was completely wrong thereby it updates its policy accordingly and after many iteration its policy get optimized where the machine gives all the results correctly now let's see an example of how Pablo trained his dog using the reinforcement ring this should make things easier for you to understand okay so what Pablo did he integrated The Learning in four different stages initially Pavlo gave me to his dog and in response to the meal the Salvation in dog got increased in the next phase what he did he created a sound with a bell but did not give him the food so this time the dog did not respond anything so in the third phase what he did he tried to condition his dog by ringing the bell and then giving him the food now seeing the food the dog started salivating again eventually what happened the dog started to salivate just after hearing the Bell even if the food was not given to him why did it happen so it happened because the dog was reinforced that whenever the master will ring the bell he is going to get the food okay I hope the concept of reinforcement learning is completely clear to you okay by now you have attained some understanding of what is machine learning and you are ready to move ahead now we have covered the basics of machine learning I'm sure you might be wondering what you can do with machine learning so let me connect you to the real life and tell you what all are the things which you can easily do using the concepts of machine learning so you can easily get answer to the questions like which types of house lies in this segment or what is the market value of this house or is this a mail a Spam or not a Spam is there any fraud well these were some of the question you could ask to the machine but for getting an answer to these you need some algorithm the machine need to train on the basis of some algorithm okay but how will you decide which algorithm to choose and when okay so the best option for us is to explore them one by one so the first is classification algorithm where the category is predicted using the data if you have some question like is this person a male or a female or is this male a Spam or not a Spam then these category of question would fall under the classification algorithm classification is a supervised learning approach in which the computer program learns from the input given to it and then uses this learning to classify new observation some examples of classification problems are speech recognization and writing recognization biometric identification document classification Etc shall we move ahead okay so next is the anomaly detection algorithm where you identify the unusual data point so what is anomaly detection well it's a technique that is used to identify unusual pattern that do not conform to expected Behavior or you can say the outliers it has many application in business like intrusion detection like identifying strange patterns in the network traffic that could signal a hack or system Health monitoring that is spotting a deadly tumor in the MRI scan or you can even use it for fraud detection credit card transaction or to deal with fall detection in operating environment so next comes the clustering algorithm you can use this clustering algorithm to group the data based on some similar condition now you can get answer to which type of houses lies in this segment or what type of customer buys this product the clustering is a task of dividing the population or data points into number of groups such that the data point and the same groups are more similar to other data points in the same group than those and the other groups in simple words the aim is to segregate groups with similar trait and assign them into cluster now this clustering is a task of dividing the population or data points into a number of groups such that the data points in the X group is more similar to the other data points in the same group rather than those in the other group in other words the aim is to segregate the groups with similar traits and assign them into different clusters let's understand this with an example suppose you are the head of a rental store and you wish to understand the preference of your customer to scale up your business so is it possible for you to look at the detail of each customer and design a unique business strategy for each of them definitely not right but what you can do is to Cluster all your customer sent to 10 different groups based on their purchasing habit and you can use a separate strategy for customers in each of these 10 different groups and this is what we call clustering next we have regression algorithm where the data itself is predicted question you ask to this type of model is like what is the market value of this house or is it going to rain tomorrow or not so regression is one of the most important and broadly used machine learning and statistics tool it allows you to make prediction from data by learning the relationship between the features of your data and some observed continuous valued response regression is used in a massive number of application you know what stock prices prediction can be done using regression now you know about different machine learning algorithm how will you decide which algorithm to choose and when so let's cover this part using a demo so in this demo part what we'll do we'll create six different machine learning model and pick the best model and build the confidence such that it has the most reliable accuracy so for our demo part we'll be using the iris data set this data set is quite very famous and is considered one of the best small project to start with you can consider this as a hello world data set for machine learning so this data set consists of 150 observation of Iris flour there are four Columns of measurement of flowers in centimeters the fifth column being the species of the flower observed all the observed flowers belong to one of the three species of Iris setosa Iris virginica and Iris versus well there's a good project because it is so well to understand the attributes are numeric so you have to figure out how to load and handle the data it is a classification problem there by allowing you to practice with perhaps an easier type of supervised learning algorithm it has only four attributes and 150 rows meaning it is very small and can easily fit into the memory and even all of the numeric attributes are in same unit and the same scale it means you do not require any special scaling or transformation to get started so let's start coding and as I told earlier for the demo part I'll be using Anaconda with python 3.0 install on it so when you install Anaconda how your Navigator would look like so there's my home page of my anaconda navigator on this I'll be using the Jupiter notebook which is a web-based interactive Computing notebook environment which will help me to write and execute my python codes on it so let's hit the launch button and execute our Jupiter notebook so as you can see that my Jupiter notebook is starting on localhost double eight nine zero okay so this is my Jupiter notebook what I'll do here I'll select new notebook python 3. does my environment where I can write and execute all my python codes on it so let's start by checking the version of the libraries in order to make this video short and more interactive and more informative I've already written this set of codes so let me just copy and paste it down I'll explain you then one by one so let's start by checking the version of the Python libraries okay so there's the code let's just copy it copied and let's paste it okay first let me summarize things for you what we are doing here we are just checking the version of the different libraries starting with python we'll first check what version of python we are working on then we'll check what is the version of sci-fi we are using the numpy matplotlib then Panda then scikitlearn okay so let's execute the Run button and see what are the various version of libraries which we are using hit the run so we are working on python 3.6.4 PSI Pi 1.0 numpy 1.14 matplotlab 2.12 panda is 0.22 and scikit line of version 0.19 okay so these are the version which I'm using ideally your version should be more recent or it should match but don't worry if you lag few versions behind as the apis do not change so quickly everything in this tutorial will very likely still work for you okay but in case you are getting an error stop and try to fix that error in case you are unable to find the solution for the error feel free to reach out at Eureka even after this class let me tell you this if you are not able to run the script properly you will not be able to complete this tutorial okay so whenever you get a doubt reach out to edureka and just resolve it now if everything is working smoothly then now it's the time to load the data set so as I said I'll be using the iris flower data set for this tutorial but before loading the data set let's import all the modules function and the object which we are going to use in this tutorial same I have already written the set of code so let's just copy and paste them let's load all the libraries so these are the various libraries which we'll be using in our tutorial so everything should work fine without an error if you get an error just stop you need to work on your cyber environment before you continue any further so I guess everything should work fine let's hit the Run button and see okay it worked so let's now move ahead and load the data we can load the data direct from the UCI machine learning repository first of all let me tell you we are using Panda to load the data okay so let's say my URL is this so This is My URL for the UCR machine learning repository from where I'll be downloading the data set okay now what I'll do I'll specify the name of each column when loading the data this will help me later to explore the data okay so I'll just copy and paste it down okay so I'm defining a variable names which consist of various parameters including separent separate petal and petal width and class so these are just the name of column from the data set okay now let's define the data set so data set equals Panda dot read underscore CSV inside that we are defining URL and the names that is equal to name as I already said we'll be using Panda to load the data all right so we are using Panda dot read CSV so we are reading the CSV file and inside that from where that CSV is coming from the URL which URL so there's my URL okay uh names equal names it's just specifying the names of that various columns in that particular CSV file okay so let's move forward and execute it so even our data set is loaded in case you have some network issues just go ahead and download the iris data file into your working directory and load it using the same method but yeah make sure that you change the url to the local name or else you might get an error okay yeah data set is loaded so let's move ahead and check our data set let's see how many columns or rows we have in our data set okay so let's print the number of rows and columns in our data set so our data set is dataset dot shape what this will do it will just give you the numbers of total number of rows and total number of column or you can say the total number of instances or attributes in your data set fine so print dataset.shape or you are getting 150 and 5. so 150 is the total number of rows in your data set and 5 is the total number of columns fine so moving on ahead what if I want to see the sample data set okay so let me just print the first 13 instances of the data set okay so print dataset dot head what I want is the first 30 instances fine this will give me the first 30 result of my data set okay so when I hit the Run button what I'm getting is the first 30 result okay zero to 29 so this is how my sample data set looks like petal and petal width and the class okay so this is how data set looks like now let's move on and look at the summary of each attribute what if I want to find out the count mean the minimum and the maximum values and some other percentiles as well so what should I do then for that print dataset dot describe what it will give let's see so you can see that all the numbers are the same scales of similar range between 0 to 8 centimeters right the mean value of the standard deviation the minimum value the 25 percentile 50 percentile 75 percentile the maximum value all these values lies in the range between 0 to 8 centimeter okay so what we just did is we just took a summary of each attribute now let's look at the number of instances that belong to each class so for that what we'll do print data set first of all so let's print data set and I want to group it Group by using class and I want the size of it size of each class fine and let's hit the Run okay so what I want to do I want to print print out data set how I want to get it I want it by class so Group by class okay now I want the size of each class find the size of each class so Group by class dot size and skew the run so you can see that 50 instances of Iris setups are 50 instances of Iris vertical and 50 instances of Iris virginica okay all are of data type integer of base64 fine so now we have a basic idea of our data now let's move ahead and create some visualization for it so for this we are going to create two different types of plot first would be the univariate plot and the next would be the multivariate plot so we'll be creating univated plots to better understand about each attribute and the next will be creating the multivated plot to better understand the relationship between different attributes okay so we start with some univated plot that is plot of each individual variable so given that the input variables are numeric we can create box and viscous plot for it okay so let's move ahead and create a box and viscous plot so dataset Dot Plot what kind I want it's a box okay and do I need a subplot yeah I need subplots for that so subplots equal true what type of layout do I want so my layout structure is 2 cross 2. next do I want to share my coordinates X and Y coordinates no I don't want to share it so share x equal false and even share y that two equals false okay so we have a dataset.plot kind equal box my subplots as two layout 2 cross 2 and then what I want to do it I want to see it so plot dot show whatever I created show it okay execute it now this gives us a much Clear idea about the distribution of the input attribute now what if I had given the layout to 2 cross 2 instead of that I have given it 4 cross 4 so what it will result just see fine everything would be printed in just one single row hold on guys Arya is a doubt he's asking that why we are using the share X and share y values what are these why we have assigned false values to it okay Ariel so in order to resolve this query I need to show you what will happen if I give True Values to them okay so be with me so share x equal true and share y that equals true so let's see what result we'll get you're getting it the X and Y coordinates are just shared among all the four visualization right so you can see that the Supple length and separate has y values ranging from 0.0 to 7.5 which are being shared among both the visualization so is with the petal length it has a shared value between 0.0 to 7.5 okay so that is why I don't want to share the value of X and Y so it's just giving us a cluttered visualization so Arya why I'm doing this I'm just doing it because I don't want my X and Y coordinates to be shared among any visualization okay that is why my share X and share y value are false okay let's execute it so this is a pretty much Clear visualization which gives a clear idea about the distribution of the input attributes now if you want you can also create a histogram of each input variable to get a clear idea of the distribution so let's create a histogram for it so dataset dot hist okay I need to see it so plot dot show let's see so there's my histogram and it seems that we have two input variables that have a gaussian distribution so this is useful to note as we can use the algorithms that can exploit this assumption okay so next comes the multivariate plot now that we have created the univated plot to understand about each attribute let's move on and look at the multivated plot and see the interaction between the different variables so first let's look at the scatter plot of all the attribute this can be helpful to spot structured relationship between input variables okay so let's create a scatter Matrix so for creating a scatter plot we need scatter Matrix and we need to pass our data set into it okay and then what I want I want to see it so plot dot show so this is how my scatter Matrix looks like it's like that the diagonal grouping of some pair right so this suggests a high correlation and a predictable relationship all right this was our multivated plot now let's move on and evaluate some algorithm that's time to create some model of the data and estimate the accuracy on the basis of unseen data okay so now we know all about our data set right we know how many instances and attributes are there in our data set we know the summary of each attribute now I guess we have seen much about our data set now let's move on and create some algorithm and estimate their accuracy based on the Unseen data okay now what we'll do we'll create some model of the data and estimate the accuracy based on some unseen data okay so for that first of all let's create a validation data set what is the validation data set validation data set is your training data set that will be using it to train our model fine all right so how we'll create a validation data set for creating a validation data set what we are going to do is we are going to split our data set into two point okay so the very first thing we'll do is to create a validation data set so why do we even need a validation data set so we need a validation data but know that the model we created is any good later what we'll do we'll use the statistical method to estimate the accuracy of the model that we create on the Unseen data we also want a more concrete estimate of the accuracy of the best model on unseen data by evaluating it on the actual unseen data okay confused let me simplify this for you what we'll do we'll split the loaded data into two parts the first 80 percent of the data will use it to train our model and the rest 20 will hold back as the validation data set that will use it to verify our trained model okay fine so let's define an array there's my array what it will consist of it will consist of all the values from the data set so dataset dot values okay next I'll Define a variable X which will consist of all the column from the array from 0 to 4. starting from 0 to 4 and the next variable Y which would consists of the array starting from this so first of all we will Define a variable X that will consist of the values in the arrays starting from the beginning 0 till 4 okay so these are the column which we'll include in the X variable and for a y variable I'll Define it as a class or the output so what I need I just need the fourth column that is my class column so I'll start it from the beginning and I just want the fourth column okay now I'll Define my validation size validation underscore size I'll Define it as 0.20 and I'll use a seed I'll Define seed equals six so this method seed sets the integer starting value used in generating random number okay that will Define the value of C equals six I'll tell you what is the importance of it later on okay so let me Define first few variables such as X underscore train test y underscore train and Y underscore test okay so what we want to do is Select some model okay so model underscore selection but before doing that what we have to do is split our training data set into two hours okay so the train underscore test underscore split what we want to split is the value of X and Y okay and my test size is equals to validation size which is a 0.20 correct and my random state is equal to seed so what the seed is doing here it's helping me to keep the same Randomness in the training and testing data set fine so let's execute it and see what is our result it's executed next we'll create a test harness for this we'll use 10 fold cross validation to estimate the accuracy so what it will do it will split our data set into 10 parts train on the 9 part and test on the one part and this will repeat for all combination of train and test splits okay so for that it's defined again my C that was 6 already defined and scoring equals accuracy fine so we are using the metric of accuracy to evaluate the model so what is this this is a ratio of number of correctly predicted instances divided by the total number of instances in the data set multiplied by 100 giving up percentage example it's 98 percent accurate or 99 percent accurate things like that okay so we'll be using this scoring variable when we run the build and evaluate each model in the next step so next part is building model till now we don't know which algorithm would be good for this problem or what configuration to use so let's begin with six different algorithm I'll be using logistic regression linear discriminant analysis K nearest neighbor classification and regression trees neighbors and code Vector machine well these algorithms which I am using is a good mixture of simple linear or non-linear algorithms in simple linears which included the logistic regression and the linear discriminant analysis or the non-linear part which included the k n algorithm the card algorithm that the neighbor is and the support Vector machines okay so we reset the random number c before each run to ensure that evaluation of each algorithm is performed using exactly the same data splits it ensures the result are directly comparable okay so let me just copy and paste it okay so what we are doing here we are building five different types of model we are building logistic regression linear discriment analysis K nearest neighbor Legend tree gaussian neighbors and the support Vector machine okay next what we'll do will evaluate model in each turn okay so what is this so we have six different model and accuracy estimation for each one of them now we need to compare the model to each other and select the most accurate of them all so running this script we saw the following result so we can see some of the result on the screen what is this it is just the accuracy score using different set of algorithms okay when we are using logistic regression what is the accuracy rate when we are using near discriminant algorithm what is the accuracy and so and so okay so from the output it seems that LDL algorithm was the most accurate model that we tested now we want to get an idea of the accuracy of the model on our validation set or the testing data set so this will give us a independent final check on the accuracy of the best model it is always valuable to keep a testing data set for just in case you made a overfitting to the testing data set or you made a data leak both will result in an overly optimistic result okay you can run the LDA model directly on the validation set and summarize the result as a final score a confusion Matrix and a classification report okay thank you guys this was all for this session in case you have any doubt or queries you can reach out to at Eureka support team and will help you to resolve your query thank you