Rock vs. Mine Prediction Using Sonar Data

hello guys this is darthan if you are new to this channel this channel is all about artificial intelligence and machine learning stuff so in this channel i will be posting three videos per week so two videos will be on the basics of machine learning so basic concepts and topics and one video will be a detailed use case or project in machine learning okay so the basic videos will be posted on monday evening and wednesday evening and the project video will be posted on friday evening okay so in this video we are going to discuss about one of the interesting use cases in machine learning so it is rock versus mind prediction using sonar data okay so we will do all the programming in python and you don't need to install any python software so we will be using google collaboratory so google collaboratory is a cloud-based system where you can write your python script so you just need google chrome for it so i'll show you how you can access it later in this video first of all let's try to understand more about this use case consider that is a submarine okay so there is a war going on between two countries so submarine of a country is going in you know underwater to another country and the enemy country have planted some mines in the ocean okay so mines are nothing but the explosives that explodes when some object comes in contact with it right so there can also be rocks in the ocean so the submarine needs to predict whether it is crossing a mine or a rock okay so our job is to make a system that can predict whether the object beneath the submarine is a mine or a rock okay so how this is done is the submarine since a sonar signal okay sorry a sonar uses a sonar that sends sound signals and a review switchbacks so this signal is then processed to detect whether the object is a mine or it's just a rock in the ocean okay so let's try to understand how we are going to do this first of all let's see the workflow for this project first of all we need to collect the sonar data okay so how this data is collected so what happens is in the laboratory setup an experiment can be done where the sonar is used to send and receive signals bounce back from a metal cylinder and some rocks okay so because the mines will be made of metals right so we collect this data which is nothing but the sonar data which is which we obtained from a rock and a metal cylinder okay and we use this sonar data and we feed this sonar data to our machine learning model and then our machine learning model can predict whether the object is made of metal or it is just a rock so this is the principle we are going to use in our prediction okay so first we need to collect the data okay so once we have the data we will process the data so we cannot use the data directly so we need to pre-process the data so there are various steps in that which we will do in this video so we need to analyze the data so we need to understand more about it so once we process the data we will split it into training and test data okay so why we are splitting the training and test data because so let's say there are unread examples so under instance of data we will train our machine learning model with 90 uh you know instances and we will test our uh machine learning model so we evaluate our model with another 10 data points okay so there are totally 100 data points we may use 90 data points for training and we can use another 10 or 20 data for testing our model okay so once we split our data into training and test data we feed it to our machine learning model so in this video in this use case we are going to use a logistic regression model because logistic regulation works really well for binary classification problem so this problem is a binary classification problem because we are going to predict whether the object is a rock or amine okay so we'll be using a logistic regression model and this is a supervised learning algorithm okay so once we uh train our machine learning model with the training data we will get a trained logistic regression model so this model has learned from the data on how a metal cylinder will be and how rock will be so this model can recognize it based on the sonar data now when we give a new data it can predict whether the object is just a rock or it is a mine so this is the workflow we will be uh following in python to make this you know script for this use case okay so now let's go into the coding part so before that i'll show you the data via okay so this is the data sonar data.csv so it is in a csv file so you can find this data in kaggle and other data sites like uc machine learning repositories and other sites so i will be giving the link for this file in the description of this video let's try to look at this video sorry in this uh data set so as you can see here there are a lot of numbers it's so there are a lot of columns so let's see there are how many instances so there will be almost 200 examples so as you can see here there are do not eat examples that means do not eat data points so on the in the last column we have something that tells you know r and em okay so r represents the values for rock and m represents the values of mines okay so as i told earlier this values are obtained in a laboratory setup where the sonar data is collected for a metal cylinder and for a rock so as you can see here there are several features so features represents the columns of this data set so we feed this dataset to our machine learning model and it can learn from this data on how a rock can be and our our mind can be with the help of this sonar data okay so let's see how we can make this python script so close this so as i told earlier we will be doing our programming in google collaboratory so search for google collab so this is the website for it so collab.research.google.com okay so you just need to choose new notebook here so this google collaboratory will be linked to your google drive account so if you have any collaboratory files so it will show up here so i'm going to use a new notebook so these are nothing but uh python notebooks as you may have noticed this in jupyter notebooks so it it has an extension of ipy and b so it is just like jupiter notebooks so as you can see here this is a dot pynb file which is known as python notebooks so i will change this file name as rock versus mind prediction okay now you can see this connect button here so go ahead and connect this so what happens is our runtime gets connected to a google server so it is completely free google collaboratory is completely free so you will be allocated a system of a very good storage size and very good ram so as you can see here so we have 12 gb of ram and gb of storage which is really good so it is better than most of our systems so we will be doing all our programming in google collaboratory okay okay so as you can see here this is called as a cell so we will write our python scripts in this sales so as you can see here you can give this code option to create another cell so if you give this text you can write some comments or a description about your code okay so i will tell about the features of this google cloud once you know we use it for different purposes so so as you can see here this is where we will upload our data files so i have already shown the sonar data file for you so how you can upload this so you can click this folder option here so there either you can choose this so it is to upload a particular file or you can right click here and click upload so i'll upload the sonar data okay so it is a very small data file so you can find it in kaggle or you see machine learning depository okay so as i told here our agenda here is to predict whether the object is a mine or a rock using the sonar data so first of all we need to import the libraries we want to import the dependencies so we will require several functions and libraries for this so i'll write a comment here so as i told you told you earlier so this is for writing description and comment about your code so just type importing the dependencies okay so you can press enter or you can place press shift press enter to complete it and go to the next cell okay so once you write a python script you can click here to run your code or you can press shift plus enter to run this code and go to the next cell okay so first we need to import some libraries so we will require numpy for this so i'll import numpy smp import numpy smp and we also need pandas so import pandas as pd okay so numpy is basically for uh you know for arrays and pandas is for several data processing steps so we will see about that later now we need a train test split okay so we have seen earlier that we need to split our data into training data under test data so we will require a function for that so from sp loan dot modern selection input train test split okay so this function is used to split our data into training and the test data so we don't need to do it manually okay so then we need our logistic regression model so sqlon is a very good uh python library for machine learning algorithms and other functions so we will encounter it in various different uh places here there is a small mistake here scale on dot model selection so input train test split now we need to import our logistic regression model so scale learn dot linear model so this is how you can import logistic regression so input logistic regression and we need the function accuracy score so from scale on dot matrix import accuracy score so this is used to find the accuracy of our model okay so these are the libraries and functions we need so first we import numpy so number is used for creating numpy arrays so and pandas so pandas is used for uh loading our data loading our data and numbers into a good table so these tables are called as data frames so we will discuss about this that in a later point so then we have a train test split so we import it from the library scale on then we have imported the logistic regression model then we have imported the function accuracy score so you can press shift press enter to run this cell and go to the next cell okay so if you have any printing output it will show here okay now let's do the data collection and processing steps so i'll just put a text here data collection and data processing okay so we already uploaded the data there are several methods to you know upload data in google collaboratory we can uh upload the data straight to collaboratory using some apis so we can do it with kaggle apis so we'll discuss about it in some other project video so as you can see here we have the sonar data file here so now we need to import this sonar data into a pandas data frame okay so i'll make a comment so in python if you write something prefixed by hash you can comment it so loading the data set to a pandas data frame so i'll create a variable called as sonar data i will be loading this uh data to a data frame and i have named this data frame as sonar data so as you can see here i have imported pandas as pd so this is just like an abbreviation so i'll be using this application so pd dot read csv so as you as i have told you earlier we have the data file as a csv file so we need to use the function read csv okay so now we need to mention the name of the file name on the location of the file so you can do it by you can go here and click here so there you will see this option called as copy path so this will copy the path and name of the file so we have to enclose it in quotes and put it in this brackets then so as you can see here we don't have a header file for this so as you can see here we don't have any editor files so either files means a name for the columns right so there is no header file so we need to mention in our pandas data frame that there is no header so editor is equal to none so i'll press shift plus enter and this loads our data to a pandas data frame okay now let's uh have a small look at our dataset so i'll just type sonar data dot yet so what this function it does is it displays the first five uh rows of our data set okay so i'll run this so as you can see here we have uh first five rows of our data set and there are several columns so as you can see here there are 59 columns but actually it's 60 column because in python the indexing starts from zero so totally we have 61 columns and 59 columns we have 59 features and we have in the last last column we have uh r or m so i have shown you right it's either rock or mine so it is that categorical value so this is the use of this get function it tries to you know it prints the first five rows of our data set now what we will do is let's try to see how many rows and columns are there so number of rows and columns so if you if you are not you know if you don't understand any functions you can just search in google about let's see so you want to know what this read csv function does so you just can go to the pandas documentation so pandas dot read csv so this is the pandas official documentation page so you can go here so you will uh so you can see the use of this uh particular function here so as you can see here it read a comma separated value csv file into a data frame it supports optional iterating or breaking the file into chunks so you can do this for any functions so in order to learn what this function actually does so these are the parameters so we don't need all these parameters so in our case we have used only two parameters which are the path of the file and we have included that there is no adder okay so if you have any doubt about any function you can search it in google like this okay so now we need to find the number of rows and columns so we can use it you can we can find it by using the function called as shape so sonar data dot shape so this gives us how many uh rows and columns are there so totally we have two not eight columns and sorry two not eight rows and sixty one columns the last 60 first column tells us whether it is a rock or a mind and there are totally two not eight rows so two not eight rows means there are two naughty instances or examples of data okay so on 61 represents the feature so let's say for this zero zeroth instance so it is a value for one rock and there are 60 features for this one rock and it is labeled as har okay so like this there are two not eight instances now what we will do is let's try to get some [Music] um statistical definitions for this data so shown our data dot descript so this gives the mean standard deviation and other parameters for our data sorry just made a small mistake here so now data.describe so as you can see here it gives the count so account represents the number of uh instances we have for the 0th column so like this we have uh all the way up to 59th column so it gives us the count so the number of uh values we have the mean of this column standard deviation for this column minimum value 25th percentile 50th percentile 75th percentile and what is the maximum value so percentile means like uh 25 percentage of the values are less than this 0.0133 for first columns and 50 percent means 50 percentage of the values are less than 0.022 so that is what percentile is so for some use cases uh it is really important for us to find this mean and standard deviation it it gives us a better understanding of the data so hence you can use this describe function to get some statistical measures so i'll just make a comment here so describe gives statistical measures of the data okay now let's try to find how many examples are there for rog and how many examples are there for mains okay so we can do that by sonar data dot value counts so this value counts function uh gives us how many rock and how many main examples are there okay so i have to include one more thing so we just need to put a 60 here the 60 is nothing but the column index so as you can see here the rock and mines are specified in the 60th column so i'm specifying 60 in this value count so value count function okay so why i am using sonar data because i have loaded this data frame into a variable called as sona data so that's why i'm using this sonar data and uh including the function with this okay so now let's see how many rock and mine examples are there so as you can see here there are totally 111 examples are there for mine and 97 examples are there for rock so it is almost equal so it is not a problem so if we have uh data for one type of instance more let's say for example if we have a thousand examples for mine and we have only 500 examples for rock then our prediction won't be very much good okay so if we have almost equal number of example for both the two categories so our prediction will be really good and we will get a very good accuracy score and our model performs well okay so it is almost uh equal here so actually uh speaking so totally there are almost 298 instances but this is not sufficient for a machine learning model so we may require even thousands and several thousands of examples for making a better model but we are just looking at some examples so we are okay with this so you just need to note one thing more the data more accurate your model is okay so i just represent here that m represents mine and r represents rob okay now let's try to group this data based on mine and rock so shown our data dot group by 60 dot me so now i'll explain you what what is the use of this so as you can see here so we got the mean value for all the columns for mine for a mine we have the mean value for 0th column 0.034 but for rock it is 0.022 as you can see here there is quite a difference between these two so this difference is really important for us because using this only we are going to predict whether the object is either a mine or a rock okay so we just found the mean for each of the 59 columns sorry 60 columns okay so the mean value for mine is these values and for rock is these values and there is a quite difference between them okay so this is really important for us now let's try to um separate the data and the labels okay so here the data i mean the these numerical values so these are the features and the last column is the label so we need to separate this so we are going to do that let's see so i'll just make a comment here separating data and label so this is a supervised learning problem where we train our machine learning model with data and labels so in unsupervised learning we don't use labels so here we have labels which are nothing but rock and mine okay so let's put all the data in the variable x so so now sona data so i am going to drop the last column 60th column so data dot drop columns is equal to 60 so i am dropping 60th column and if i am dropping a column i need to specify the axis as one so if i am dropping a row i will be specifying access as zero okay and let's put the label in the variable y so sonar data so we need to use a square bracket here 60 okay so what i'm basically doing here is i'm storing all the values in x except the last column so i'm dropping the 60th column and i'm storing storing the 60th column in y okay let's try to print and see this so print x print y so as you can see here now there are only 59 columns so actually it's 60 column and it starts with zero and the last label is in the variable called space so we have successfully splitted the data and the labels okay now what we will do is we will try to split this data into training and test data okay so let's include a text here training and test data so as we have already uh imported the function drain test split so we will be using this function to split our data okay so we need to give some variable names here extreme so you can give any names here so for convenient purpose i'm giving this name so explain x test y train and y test so this order should be followed so first we will take the training data on the test data then we will take the labels of training data labels of test data is equal to train test split and we have to include this x and y here so we are going to split this x and y into training and test data so x comma y so there are several parameters here so i'll explain you about that so x comma y test size let's have the test size as 0.1 and stratify stratified is equal to y and random state c equal to let's say okay so now let's try to understand about these parameters so we are going to split our data into x train and x test y train and y test so x train is nothing but the training data for this and extess is the testing data and white train is the label of those training data and y test is the label of the test data okay so now we are using the function train test split then in the parameters we have included x and y so we are going to split this x and y into training and test data so here we have the parameter test size so test says like if we give 0.1 means we need 10 percentage of the data to be test data okay say for example we add uh almost 200 examples so what happens is like 10 percentage of 200 is 20 so we will have 20 test data so that is the use of this test size you can use 0.1 or 0.2 so based on the number of data you have okay so here we will take just 10 percentage of our data stream the stratify is equal to why so stratify why we are using this stratifies yes we need to split the data based on rock on mine say for example we need to have equal almost equal number of rocks in tested training data and equal number of mines in the training data okay so hence we include this stratify so our data will be splitted based on the number of uh these two rock row conveying okay and then we have random state so random state is to split the data in a particular order say for example if you do the same code and in the code you include one your data will be splitted uh you know in the same way as my data splitter so if ah i put two here so my data will be splitted in a specific way and if you include two in your code it will also be splitted in the same way it is basically to reproduce the code as it is so i'll use one okay so now we can split our data okay now let's try to see how many uh training data text test data are there so print x dot shape so it is the original data without splitting into train and test and then we have extreme dot shape and fix test dot shape so i'll run this so as you can see here in the original x we have two not eight examples and in the training data we have 187 instance and in the test data we have 21 instance okay so we have 21 test data and 187 training data now we need to train our machine learning model with this extreme with this training data okay now let's see how we can train our model so model training so we will be using a logistic regression model so i'll create a variable called as model so as you can see here we have already imported the logistic regression function here so model is equal to logistic regression so this will load this logistic regression function into the variable model now we are going to train the models for training the logistic regression model with training data okay so for that we use the function model.fit here we need to include the training data and training label okay which are extreme and white ring okay so let's see what is this extreme unwind train once so that you can understand it so i'm printing extreme and also white rain so this is the training data so exchange is the training data and white line is the training label so as you can see here we have the data here so totally there are 187 examples and 60 columns and this is the label so as you can see here it is kind of random because we have used the training testing spread okay so now we are going to feed this training data on this training data label to our logistic regression model so that's why i've included model.fit xtrain and white rain so when i run this our model will be drained so if you are having a lot of data and if you are using a complex model like a neural network it will take a lot of time so here we are seeing a simple example and simple data so it doesn't take much time okay so our model will be trained so these are some parameters of our model now let's see how our model is predicting so now let's check the accuracy score of our model so now we are going to evaluate our model so model evaluation so we have imported the function accuracy score for it so we will use this function to find the accuracy of our model accuracy on training data so first let's find the accuracy on the training data so what's happening here is so we will use this same data the data which the machine learning model has learned so we will try to use this model to predict these examples okay so then we will use the test data okay so what is the significance here is the model has already seen what is this test data sorry the training data but it it haven't seen what is this test data okay it is just like preparing for exam let's say you are preparing all the example problems in a mathematics book for your exam so those example problems are nothing but the training data so in the exam a new problem will be asked and you need to solve that but but you have never seen that question right so that is nothing but that is data so we need to test our model for accuracy on training data and accuracy on test data so it is always in most of the cases the accuracy on training data will be more because the model has already seen uh this training data and uh most of the times the accuracy and test data will be less okay so if the accuracy of your model is somewhere greater than 70 which is it is actually good so and it also depends on the amount of data you you use so as i have told earlier so if you use uh you know many data points and if you use a better model you will get a good accuracy score okay so if we use uh you know quite less number of data as we have in this case where we have only 200 data points our accuracy can be low okay so but the idea of this you know video is for you to understand how you can implement these steps so the accuracy is not that much important but we have to note here is so any accuracy greater than 70 percentage is good okay so now let's try to predict the training data extreme prediction is equal to model dot predict next extreme training data accuracy so we will store this accuracy value in this variable training data accuracy so training data accuracy is equal to accuracy score so this is the extreme prediction is the prediction our model makes okay based on uh its learning so we need to include extreme prediction and all the correct values it is white ring so okay so what happens here is we are going to compare the we are going to get the accuracy score so extreme prediction is the prediction of our model and y train is the real answer of our answer of uh this instances so as you know here so we have uh we have this test data right so x test and y test is the label of this test data so now what happens is we are going to compare the prediction of our model and the original label of these data okay by that we will get the accuracy score so let's try to get the accuracy score for the training data so i'll print the accuracy score so accuracy on training data and copy this so this will have the accuracy score here training data accuracy so as you can see here we got almost 83.4 percentage of accuracy so it is actually kind of good for these many data so now let's try to find the accuracy score of test data okay so it will be the same part except for some changes we just need to include the test data here so accuracy on test data so x test prediction test data accuracy so the model has never seen this data okay so model dot predict x test okay so y test so now we are using our model to predict the test data and this uh prediction of this model will be compared to the original labels which is why test okay is data accuracy now we need to print this accuracy score so accuracy on test data is data accuracy so we got a 76 percentage as accuracy score which is really good so which means out of 100 times it can predict 75 times the correct object whether it is a rock or mine okay so we got accuracy score as 83 percentage for training data and 80 76 percentage for test data so our model is performing fine okay so now let's what we are going to do is so we have a trained uh logistic regression model and now we are going to make a predictive system that can predict whether the object is either rock or it is mine using that sonar data okay so now let's see how we can make this predictive system so making a predictive system so we need to give the input data so i'm making a variable called as input data okay so in this parenthesis we need to include the sonar data so we have seen this sonar data right so we will be taking some examples for rock and mine and we will check whether our model is predicting uh the rock and mine correctly okay so this is the use of this code snippet so once i complete the script i'll uh copy some values and put it and let's see whether it's predicting correctly so input data so once we get the input data we have to convert it to a numpy array because the processing and non numpy array is faster and it's more uh easy so it's basically changing the data type to a numpy array so at least to do a numpy array so changing the input data so just make a comment here so changing the input data to a numpy array so we will use the function numpy.s array for this function so input data as numpy array so this is the variable i am giving it so input data's numper is equal to np dot s array so if you remember we have imported the library numpy as np so i am using np instead of numpy okay so np dot ask array input data so basically we are converting this list into a numpy array and let's let's take an example say for example um i'll open this from our notepad okay so let's take some random example okay let's take it i think it is a rock so as you can see here this example is stock so if we feed this data to our machine learning model so it should predict that this is a rock okay so i'll copy this okay so i'll put this in this input data so we have totally 60 features so i have converted this input data so this is basically a list so we are converting it to a numpy array okay now we need to reshape the data okay so because we are just predicting for uh one instance so for that purpose we need to reshape this array otherwise our model can be confused by the number of data points so we need to reshape the numpy array as we are predicting for one instance okay so input data reshape is equal to i'm copying this input data's number so we need to reshape this reshape one comma minus one so this one comma minus one represents there is one instance and we are going to predict uh the label for this one instance so that is why we are reshaping this so once we reshape it we need to make the prediction so i will create a variable called as prediction and i'll store the prediction of our model so model dot predict function is used to edit it so we have stored our trained logistic regression model in the variable called as model so as you can see here so i am calling that model function so model variable so model.predict this input data reset so this contains the features of our data okay so this data is present in input data reshape okay so basically what happens is that this model dot predict returns either r or m as value okay so it uh you know tells us whether it is either a rock or a mine okay now let's try to print this prediction so let's try and run this so you should predict that this object represents a rock because we have copied the data for a log right so i'll run this and yeah it predicted correctly that the object represents rock from this example okay and let's include a if condition here here that if we get r as our prediction it should say that the object is a rock and if we get m the object is a mine okay so as you can see here this r is included in a list so if this prediction is equal to r prediction 0 is equal to r so i am using this 0 because this is 0 represents the first element of the list here the list is this prediction okay so the first element is r and we need to represent it with this index as 0 so that's why i'm using 0 as the index here so if it is not in a list i won't include this as 0 okay so if the first element of this list is equal to r we need to print that the object is rob okay otherwise so else we need to print it as a mine so when it is m we need to print it is the object smile okay let's try to run this so as you can see here so we get the first element in the list as r so it tells us the object is a rock and we know that we copied the data of a rock now let's try to see whether our model is predicting the mind correctly so let's try to get some random value for mine and let's try to let's find whether our model is predicting correctly i'll take this value so this is this value represents mine as you can see here there is an m so i'll copy this value let's see whether it's predicting correctly so if if our model is working correctly it should say that uh it is m which means that the object is a rock so i'll just replace this okay now let's try to run this now it should say that say that the object is a mine so as you can see here it is predicting correctly that the object is a mine so this is how our predicting system works so i hope you have understood all the process we have done here so i'll just give you a short recap of how we are doing this so first we have imported the dependencies so numpy is used for uh making arrays and pandas is used for making a data frames and we are using the libraries scalon for using the function drain test split so it is used to split our data into training and test data and in this case we are using a logistic regression model so we are importing that logistic regression function from sklearn.linear model and we are importing the accuracy score to find the accuracy score of our model from scaling.matrix then data collection processing so we have imported the sonar.csv file into our google collab environment okay so now we are feeding it to a pandas data frame by pd.read csv function so and i am storing that data frame in a variable called as sonar data so here we need to give the path of the file and since we have no editor in this file so we have to mention that there is none and by the function yet we are just printing the first five columns of our pandas data frame and we find that the last column is a categorical column which says whether it is a rock or a mine so r represents stock and m represents mine then we are determining the number of uh rows and columns we have so we have two not eight columns which represents two not eight data points so and uh 61 features okay so 60 features on one label which is rock or mine then we have used the function sonar data.describe which gives us the count the number of values we have mean and standard deviation and other statistical values then we are counting how many main examples are then how many rock examples are there and we find out that it is almost equal and then and then we are grouping the data based on mine and rock and we find their mean values and we get a quite difference in their mean values okay so as you can see here there is a difference in mean value of rock and mine for each of the column okay so now we are splitting the data into all the features and all on the labels so we are feeding all the features to the variable x and all the labels to the variable y okay now we are splitting our uh x and y our data into training and test data okay so the training data is used to train our model and our model is evaluated with the help of test data right and then we are loading our logistic regression model in the variable model and uh by the function model dot fit our model is trained it is just like a graph okay so in the x axis there will be this features under y-axis there will be labels okay and uh the graph will be plotted so this is how model is trained so once we have trained our model using the function model.fit we are finding the accuracy score so first we find the accuracy score of uh training data which is around 85 84 percentage and then we find the accuracy score for the test data which is around 76 percentage then we are making a predictive system where if we give the features if we give the data it can predict whether the object is a rock or mi okay so this is so these are these are all the procedures we have used in this use case so i hope you are clear with all these so i will be giving uh the link for the sonar data so the data file and also this follow-up file in the link of this link in the description of this video so you can download it from here so do try google collab so try to do all these things we have done here okay so try to write all this python script by your own and try to understand it so if you have any doubt if you if you run into any problems uh mentioned in the comment i will try to solve your problem okay so that's it from my side i hope you have understood the topic we are covered in this video so i'll see you in the next video thanks for watching

hello guys this is darthan if you are new to this channel this channel is all about artificial intelligence and machine learning stuff so in this channel i will be posting three videos per week so two videos will be on the basics of machine learning so basic concepts and topics and one video will be a detailed use case or project in machine learning okay so the basic videos will be posted on monday evening and wednesday evening and the project video will be posted on friday evening okay so in this video we are going to discuss about one of the interesting use cases in machine learning so it is rock versus mind prediction using sonar data okay so we will do all the programming in python and you don&#39;t need to install any python software so we will be using google collaboratory so google collaboratory is a cloud-based system where you can write your python script so you just need google chrome for it so i&#39;ll show you how you can access it later in this video first of all let&#39;s try to understand more about this use case consider that is a submarine okay so there is a war going on between two countries so submarine of a country is going in you know underwater to another country and the enemy country have planted some mines in the ocean okay so mines are nothing but the explosives that explodes when some object comes in contact with it right so there can also be rocks in the ocean so the submarine needs to predict whether it is crossing a mine or a rock okay so our job is to make a system that can predict whether the object beneath the submarine is a mine or a rock okay so how this is done is the submarine since a sonar signal okay sorry a sonar uses a sonar that sends sound signals and a review switchbacks so this signal is then processed to detect whether the object is a mine or it&#39;s just a rock in the ocean okay so let&#39;s try to understand how we are going to do this first of all let&#39;s see the workflow for this project first of all we need to collect the sonar data okay so how this data is collected so what happens is in the laboratory setup an experiment can be done where the sonar is used to send and receive signals bounce back from a metal cylinder and some rocks okay so because the mines will be made of metals right so we collect this data which is nothing but the sonar data which is which we obtained from a rock and a metal cylinder okay and we use this sonar data and we feed this sonar data to our machine learning model and then our machine learning model can predict whether the object is made of metal or it is just a rock so this is the principle we are going to use in our prediction okay so first we need to collect the data okay so once we have the data we will process the data so we cannot use the data directly so we need to pre-process the data so there are various steps in that which we will do in this video so we need to analyze the data so we need to understand more about it so once we process the data we will split it into training and test data okay so why we are splitting the training and test data because so let&#39;s say there are unread examples so under instance of data we will train our machine learning model with 90 uh you know instances and we will test our uh machine learning model so we evaluate our model with another 10 data points okay so there are totally 100 data points we may use 90 data points for training and we can use another 10 or 20 data for testing our model okay so once we split our data into training and test data we feed it to our machine learning model so in this video in this use case we are going to use a logistic regression model because logistic regulation works really well for binary classification problem so this problem is a binary classification problem because we are going to predict whether the object is a rock or amine okay so we&#39;ll be using a logistic regression model and this is a supervised learning algorithm okay so once we uh train our machine learning model with the training data we will get a trained logistic regression model so this model has learned from the data on how a metal cylinder will be and how rock will be so this model can recognize it based on the sonar data now when we give a new data it can predict whether the object is just a rock or it is a mine so this is the workflow we will be uh following in python to make this you know script for this use case okay so now let&#39;s go into the coding part so before that i&#39;ll show you the data via okay so this is the data sonar data.csv so it is in a csv file so you can find this data in kaggle and other data sites like uc machine learning repositories and other sites so i will be giving the link for this file in the description of this video let&#39;s try to look at this video sorry in this uh data set so as you can see here there are a lot of numbers it&#39;s so there are a lot of columns so let&#39;s see there are how many instances so there will be almost 200 examples so as you can see here there are do not eat examples that means do not eat data points so on the in the last column we have something that tells you know r and em okay so r represents the values for rock and m represents the values of mines okay so as i told earlier this values are obtained in a laboratory setup where the sonar data is collected for a metal cylinder and for a rock so as you can see here there are several features so features represents the columns of this data set so we feed this dataset to our machine learning model and it can learn from this data on how a rock can be and our our mind can be with the help of this sonar data okay so let&#39;s see how we can make this python script so close this so as i told earlier we will be doing our programming in google collaboratory so search for google collab so this is the website for it so collab.research.google.com okay so you just need to choose new notebook here so this google collaboratory will be linked to your google drive account so if you have any collaboratory files so it will show up here so i&#39;m going to use a new notebook so these are nothing but uh python notebooks as you may have noticed this in jupyter notebooks so it it has an extension of ipy and b so it is just like jupiter notebooks so as you can see here this is a dot pynb file which is known as python notebooks so i will change this file name as rock versus mind prediction okay now you can see this connect button here so go ahead and connect this so what happens is our runtime gets connected to a google server so it is completely free google collaboratory is completely free so you will be allocated a system of a very good storage size and very good ram so as you can see here so we have 12 gb of ram and gb of storage which is really good so it is better than most of our systems so we will be doing all our programming in google collaboratory okay okay so as you can see here this is called as a cell so we will write our python scripts in this sales so as you can see here you can give this code option to create another cell so if you give this text you can write some comments or a description about your code okay so i will tell about the features of this google cloud once you know we use it for different purposes so so as you can see here this is where we will upload our data files so i have already shown the sonar data file for you so how you can upload this so you can click this folder option here so there either you can choose this so it is to upload a particular file or you can right click here and click upload so i&#39;ll upload the sonar data okay so it is a very small data file so you can find it in kaggle or you see machine learning depository okay so as i told here our agenda here is to predict whether the object is a mine or a rock using the sonar data so first of all we need to import the libraries we want to import the dependencies so we will require several functions and libraries for this so i&#39;ll write a comment here so as i told you told you earlier so this is for writing description and comment about your code so just type importing the dependencies okay so you can press enter or you can place press shift press enter to complete it and go to the next cell okay so once you write a python script you can click here to run your code or you can press shift plus enter to run this code and go to the next cell okay so first we need to import some libraries so we will require numpy for this so i&#39;ll import numpy smp import numpy smp and we also need pandas so import pandas as pd okay so numpy is basically for uh you know for arrays and pandas is for several data processing steps so we will see about that later now we need a train test split okay so we have seen earlier that we need to split our data into training data under test data so we will require a function for that so from sp loan dot modern selection input train test split okay so this function is used to split our data into training and the test data so we don&#39;t need to do it manually okay so then we need our logistic regression model so sqlon is a very good uh python library for machine learning algorithms and other functions so we will encounter it in various different uh places here there is a small mistake here scale on dot model selection so input train test split now we need to import our logistic regression model so scale learn dot linear model so this is how you can import logistic regression so input logistic regression and we need the function accuracy score so from scale on dot matrix import accuracy score so this is used to find the accuracy of our model okay so these are the libraries and functions we need so first we import numpy so number is used for creating numpy arrays so and pandas so pandas is used for uh loading our data loading our data and numbers into a good table so these tables are called as data frames so we will discuss about this that in a later point so then we have a train test split so we import it from the library scale on then we have imported the logistic regression model then we have imported the function accuracy score so you can press shift press enter to run this cell and go to the next cell okay so if you have any printing output it will show here okay now let&#39;s do the data collection and processing steps so i&#39;ll just put a text here data collection and data processing okay so we already uploaded the data there are several methods to you know upload data in google collaboratory we can uh upload the data straight to collaboratory using some apis so we can do it with kaggle apis so we&#39;ll discuss about it in some other project video so as you can see here we have the sonar data file here so now we need to import this sonar data into a pandas data frame okay so i&#39;ll make a comment so in python if you write something prefixed by hash you can comment it so loading the data set to a pandas data frame so i&#39;ll create a variable called as sonar data i will be loading this uh data to a data frame and i have named this data frame as sonar data so as you can see here i have imported pandas as pd so this is just like an abbreviation so i&#39;ll be using this application so pd dot read csv so as you as i have told you earlier we have the data file as a csv file so we need to use the function read csv okay so now we need to mention the name of the file name on the location of the file so you can do it by you can go here and click here so there you will see this option called as copy path so this will copy the path and name of the file so we have to enclose it in quotes and put it in this brackets then so as you can see here we don&#39;t have a header file for this so as you can see here we don&#39;t have any editor files so either files means a name for the columns right so there is no header file so we need to mention in our pandas data frame that there is no header so editor is equal to none so i&#39;ll press shift plus enter and this loads our data to a pandas data frame okay now let&#39;s uh have a small look at our dataset so i&#39;ll just type sonar data dot yet so what this function it does is it displays the first five uh rows of our data set okay so i&#39;ll run this so as you can see here we have uh first five rows of our data set and there are several columns so as you can see here there are 59 columns but actually it&#39;s 60 column because in python the indexing starts from zero so totally we have 61 columns and 59 columns we have 59 features and we have in the last last column we have uh r or m so i have shown you right it&#39;s either rock or mine so it is that categorical value so this is the use of this get function it tries to you know it prints the first five rows of our data set now what we will do is let&#39;s try to see how many rows and columns are there so number of rows and columns so if you if you are not you know if you don&#39;t understand any functions you can just search in google about let&#39;s see so you want to know what this read csv function does so you just can go to the pandas documentation so pandas dot read csv so this is the pandas official documentation page so you can go here so you will uh so you can see the use of this uh particular function here so as you can see here it read a comma separated value csv file into a data frame it supports optional iterating or breaking the file into chunks so you can do this for any functions so in order to learn what this function actually does so these are the parameters so we don&#39;t need all these parameters so in our case we have used only two parameters which are the path of the file and we have included that there is no adder okay so if you have any doubt about any function you can search it in google like this okay so now we need to find the number of rows and columns so we can use it you can we can find it by using the function called as shape so sonar data dot shape so this gives us how many uh rows and columns are there so totally we have two not eight columns and sorry two not eight rows and sixty one columns the last 60 first column tells us whether it is a rock or a mind and there are totally two not eight rows so two not eight rows means there are two naughty instances or examples of data okay so on 61 represents the feature so let&#39;s say for this zero zeroth instance so it is a value for one rock and there are 60 features for this one rock and it is labeled as har okay so like this there are two not eight instances now what we will do is let&#39;s try to get some [Music] um statistical definitions for this data so shown our data dot descript so this gives the mean standard deviation and other parameters for our data sorry just made a small mistake here so now data.describe so as you can see here it gives the count so account represents the number of uh instances we have for the 0th column so like this we have uh all the way up to 59th column so it gives us the count so the number of uh values we have the mean of this column standard deviation for this column minimum value 25th percentile 50th percentile 75th percentile and what is the maximum value so percentile means like uh 25 percentage of the values are less than this 0.0133 for first columns and 50 percent means 50 percentage of the values are less than 0.022 so that is what percentile is so for some use cases uh it is really important for us to find this mean and standard deviation it it gives us a better understanding of the data so hence you can use this describe function to get some statistical measures so i&#39;ll just make a comment here so describe gives statistical measures of the data okay now let&#39;s try to find how many examples are there for rog and how many examples are there for mains okay so we can do that by sonar data dot value counts so this value counts function uh gives us how many rock and how many main examples are there okay so i have to include one more thing so we just need to put a 60 here the 60 is nothing but the column index so as you can see here the rock and mines are specified in the 60th column so i&#39;m specifying 60 in this value count so value count function okay so why i am using sonar data because i have loaded this data frame into a variable called as sona data so that&#39;s why i&#39;m using this sonar data and uh including the function with this okay so now let&#39;s see how many rock and mine examples are there so as you can see here there are totally 111 examples are there for mine and 97 examples are there for rock so it is almost equal so it is not a problem so if we have uh data for one type of instance more let&#39;s say for example if we have a thousand examples for mine and we have only 500 examples for rock then our prediction won&#39;t be very much good okay so if we have almost equal number of example for both the two categories so our prediction will be really good and we will get a very good accuracy score and our model performs well okay so it is almost uh equal here so actually uh speaking so totally there are almost 298 instances but this is not sufficient for a machine learning model so we may require even thousands and several thousands of examples for making a better model but we are just looking at some examples so we are okay with this so you just need to note one thing more the data more accurate your model is okay so i just represent here that m represents mine and r represents rob okay now let&#39;s try to group this data based on mine and rock so shown our data dot group by 60 dot me so now i&#39;ll explain you what what is the use of this so as you can see here so we got the mean value for all the columns for mine for a mine we have the mean value for 0th column 0.034 but for rock it is 0.022 as you can see here there is quite a difference between these two so this difference is really important for us because using this only we are going to predict whether the object is either a mine or a rock okay so we just found the mean for each of the 59 columns sorry 60 columns okay so the mean value for mine is these values and for rock is these values and there is a quite difference between them okay so this is really important for us now let&#39;s try to um separate the data and the labels okay so here the data i mean the these numerical values so these are the features and the last column is the label so we need to separate this so we are going to do that let&#39;s see so i&#39;ll just make a comment here separating data and label so this is a supervised learning problem where we train our machine learning model with data and labels so in unsupervised learning we don&#39;t use labels so here we have labels which are nothing but rock and mine okay so let&#39;s put all the data in the variable x so so now sona data so i am going to drop the last column 60th column so data dot drop columns is equal to 60 so i am dropping 60th column and if i am dropping a column i need to specify the axis as one so if i am dropping a row i will be specifying access as zero okay and let&#39;s put the label in the variable y so sonar data so we need to use a square bracket here 60 okay so what i&#39;m basically doing here is i&#39;m storing all the values in x except the last column so i&#39;m dropping the 60th column and i&#39;m storing storing the 60th column in y okay let&#39;s try to print and see this so print x print y so as you can see here now there are only 59 columns so actually it&#39;s 60 column and it starts with zero and the last label is in the variable called space so we have successfully splitted the data and the labels okay now what we will do is we will try to split this data into training and test data okay so let&#39;s include a text here training and test data so as we have already uh imported the function drain test split so we will be using this function to split our data okay so we need to give some variable names here extreme so you can give any names here so for convenient purpose i&#39;m giving this name so explain x test y train and y test so this order should be followed so first we will take the training data on the test data then we will take the labels of training data labels of test data is equal to train test split and we have to include this x and y here so we are going to split this x and y into training and test data so x comma y so there are several parameters here so i&#39;ll explain you about that so x comma y test size let&#39;s have the test size as 0.1 and stratify stratified is equal to y and random state c equal to let&#39;s say okay so now let&#39;s try to understand about these parameters so we are going to split our data into x train and x test y train and y test so x train is nothing but the training data for this and extess is the testing data and white train is the label of those training data and y test is the label of the test data okay so now we are using the function train test split then in the parameters we have included x and y so we are going to split this x and y into training and test data so here we have the parameter test size so test says like if we give 0.1 means we need 10 percentage of the data to be test data okay say for example we add uh almost 200 examples so what happens is like 10 percentage of 200 is 20 so we will have 20 test data so that is the use of this test size you can use 0.1 or 0.2 so based on the number of data you have okay so here we will take just 10 percentage of our data stream the stratify is equal to why so stratify why we are using this stratifies yes we need to split the data based on rock on mine say for example we need to have equal almost equal number of rocks in tested training data and equal number of mines in the training data okay so hence we include this stratify so our data will be splitted based on the number of uh these two rock row conveying okay and then we have random state so random state is to split the data in a particular order say for example if you do the same code and in the code you include one your data will be splitted uh you know in the same way as my data splitter so if ah i put two here so my data will be splitted in a specific way and if you include two in your code it will also be splitted in the same way it is basically to reproduce the code as it is so i&#39;ll use one okay so now we can split our data okay now let&#39;s try to see how many uh training data text test data are there so print x dot shape so it is the original data without splitting into train and test and then we have extreme dot shape and fix test dot shape so i&#39;ll run this so as you can see here in the original x we have two not eight examples and in the training data we have 187 instance and in the test data we have 21 instance okay so we have 21 test data and 187 training data now we need to train our machine learning model with this extreme with this training data okay now let&#39;s see how we can train our model so model training so we will be using a logistic regression model so i&#39;ll create a variable called as model so as you can see here we have already imported the logistic regression function here so model is equal to logistic regression so this will load this logistic regression function into the variable model now we are going to train the models for training the logistic regression model with training data okay so for that we use the function model.fit here we need to include the training data and training label okay which are extreme and white ring okay so let&#39;s see what is this extreme unwind train once so that you can understand it so i&#39;m printing extreme and also white rain so this is the training data so exchange is the training data and white line is the training label so as you can see here we have the data here so totally there are 187 examples and 60 columns and this is the label so as you can see here it is kind of random because we have used the training testing spread okay so now we are going to feed this training data on this training data label to our logistic regression model so that&#39;s why i&#39;ve included model.fit xtrain and white rain so when i run this our model will be drained so if you are having a lot of data and if you are using a complex model like a neural network it will take a lot of time so here we are seeing a simple example and simple data so it doesn&#39;t take much time okay so our model will be trained so these are some parameters of our model now let&#39;s see how our model is predicting so now let&#39;s check the accuracy score of our model so now we are going to evaluate our model so model evaluation so we have imported the function accuracy score for it so we will use this function to find the accuracy of our model accuracy on training data so first let&#39;s find the accuracy on the training data so what&#39;s happening here is so we will use this same data the data which the machine learning model has learned so we will try to use this model to predict these examples okay so then we will use the test data okay so what is the significance here is the model has already seen what is this test data sorry the training data but it it haven&#39;t seen what is this test data okay it is just like preparing for exam let&#39;s say you are preparing all the example problems in a mathematics book for your exam so those example problems are nothing but the training data so in the exam a new problem will be asked and you need to solve that but but you have never seen that question right so that is nothing but that is data so we need to test our model for accuracy on training data and accuracy on test data so it is always in most of the cases the accuracy on training data will be more because the model has already seen uh this training data and uh most of the times the accuracy and test data will be less okay so if the accuracy of your model is somewhere greater than 70 which is it is actually good so and it also depends on the amount of data you you use so as i have told earlier so if you use uh you know many data points and if you use a better model you will get a good accuracy score okay so if we use uh you know quite less number of data as we have in this case where we have only 200 data points our accuracy can be low okay so but the idea of this you know video is for you to understand how you can implement these steps so the accuracy is not that much important but we have to note here is so any accuracy greater than 70 percentage is good okay so now let&#39;s try to predict the training data extreme prediction is equal to model dot predict next extreme training data accuracy so we will store this accuracy value in this variable training data accuracy so training data accuracy is equal to accuracy score so this is the extreme prediction is the prediction our model makes okay based on uh its learning so we need to include extreme prediction and all the correct values it is white ring so okay so what happens here is we are going to compare the we are going to get the accuracy score so extreme prediction is the prediction of our model and y train is the real answer of our answer of uh this instances so as you know here so we have uh we have this test data right so x test and y test is the label of this test data so now what happens is we are going to compare the prediction of our model and the original label of these data okay by that we will get the accuracy score so let&#39;s try to get the accuracy score for the training data so i&#39;ll print the accuracy score so accuracy on training data and copy this so this will have the accuracy score here training data accuracy so as you can see here we got almost 83.4 percentage of accuracy so it is actually kind of good for these many data so now let&#39;s try to find the accuracy score of test data okay so it will be the same part except for some changes we just need to include the test data here so accuracy on test data so x test prediction test data accuracy so the model has never seen this data okay so model dot predict x test okay so y test so now we are using our model to predict the test data and this uh prediction of this model will be compared to the original labels which is why test okay is data accuracy now we need to print this accuracy score so accuracy on test data is data accuracy so we got a 76 percentage as accuracy score which is really good so which means out of 100 times it can predict 75 times the correct object whether it is a rock or mine okay so we got accuracy score as 83 percentage for training data and 80 76 percentage for test data so our model is performing fine okay so now let&#39;s what we are going to do is so we have a trained uh logistic regression model and now we are going to make a predictive system that can predict whether the object is either rock or it is mine using that sonar data okay so now let&#39;s see how we can make this predictive system so making a predictive system so we need to give the input data so i&#39;m making a variable called as input data okay so in this parenthesis we need to include the sonar data so we have seen this sonar data right so we will be taking some examples for rock and mine and we will check whether our model is predicting uh the rock and mine correctly okay so this is the use of this code snippet so once i complete the script i&#39;ll uh copy some values and put it and let&#39;s see whether it&#39;s predicting correctly so input data so once we get the input data we have to convert it to a numpy array because the processing and non numpy array is faster and it&#39;s more uh easy so it&#39;s basically changing the data type to a numpy array so at least to do a numpy array so changing the input data so just make a comment here so changing the input data to a numpy array so we will use the function numpy.s array for this function so input data as numpy array so this is the variable i am giving it so input data&#39;s numper is equal to np dot s array so if you remember we have imported the library numpy as np so i am using np instead of numpy okay so np dot ask array input data so basically we are converting this list into a numpy array and let&#39;s let&#39;s take an example say for example um i&#39;ll open this from our notepad okay so let&#39;s take some random example okay let&#39;s take it i think it is a rock so as you can see here this example is stock so if we feed this data to our machine learning model so it should predict that this is a rock okay so i&#39;ll copy this okay so i&#39;ll put this in this input data so we have totally 60 features so i have converted this input data so this is basically a list so we are converting it to a numpy array okay now we need to reshape the data okay so because we are just predicting for uh one instance so for that purpose we need to reshape this array otherwise our model can be confused by the number of data points so we need to reshape the numpy array as we are predicting for one instance okay so input data reshape is equal to i&#39;m copying this input data&#39;s number so we need to reshape this reshape one comma minus one so this one comma minus one represents there is one instance and we are going to predict uh the label for this one instance so that is why we are reshaping this so once we reshape it we need to make the prediction so i will create a variable called as prediction and i&#39;ll store the prediction of our model so model dot predict function is used to edit it so we have stored our trained logistic regression model in the variable called as model so as you can see here so i am calling that model function so model variable so model.predict this input data reset so this contains the features of our data okay so this data is present in input data reshape okay so basically what happens is that this model dot predict returns either r or m as value okay so it uh you know tells us whether it is either a rock or a mine okay now let&#39;s try to print this prediction so let&#39;s try and run this so you should predict that this object represents a rock because we have copied the data for a log right so i&#39;ll run this and yeah it predicted correctly that the object represents rock from this example okay and let&#39;s include a if condition here here that if we get r as our prediction it should say that the object is a rock and if we get m the object is a mine okay so as you can see here this r is included in a list so if this prediction is equal to r prediction 0 is equal to r so i am using this 0 because this is 0 represents the first element of the list here the list is this prediction okay so the first element is r and we need to represent it with this index as 0 so that&#39;s why i&#39;m using 0 as the index here so if it is not in a list i won&#39;t include this as 0 okay so if the first element of this list is equal to r we need to print that the object is rob okay otherwise so else we need to print it as a mine so when it is m we need to print it is the object smile okay let&#39;s try to run this so as you can see here so we get the first element in the list as r so it tells us the object is a rock and we know that we copied the data of a rock now let&#39;s try to see whether our model is predicting the mind correctly so let&#39;s try to get some random value for mine and let&#39;s try to let&#39;s find whether our model is predicting correctly i&#39;ll take this value so this is this value represents mine as you can see here there is an m so i&#39;ll copy this value let&#39;s see whether it&#39;s predicting correctly so if if our model is working correctly it should say that uh it is m which means that the object is a rock so i&#39;ll just replace this okay now let&#39;s try to run this now it should say that say that the object is a mine so as you can see here it is predicting correctly that the object is a mine so this is how our predicting system works so i hope you have understood all the process we have done here so i&#39;ll just give you a short recap of how we are doing this so first we have imported the dependencies so numpy is used for uh making arrays and pandas is used for making a data frames and we are using the libraries scalon for using the function drain test split so it is used to split our data into training and test data and in this case we are using a logistic regression model so we are importing that logistic regression function from sklearn.linear model and we are importing the accuracy score to find the accuracy score of our model from scaling.matrix then data collection processing so we have imported the sonar.csv file into our google collab environment okay so now we are feeding it to a pandas data frame by pd.read csv function so and i am storing that data frame in a variable called as sonar data so here we need to give the path of the file and since we have no editor in this file so we have to mention that there is none and by the function yet we are just printing the first five columns of our pandas data frame and we find that the last column is a categorical column which says whether it is a rock or a mine so r represents stock and m represents mine then we are determining the number of uh rows and columns we have so we have two not eight columns which represents two not eight data points so and uh 61 features okay so 60 features on one label which is rock or mine then we have used the function sonar data.describe which gives us the count the number of values we have mean and standard deviation and other statistical values then we are counting how many main examples are then how many rock examples are there and we find out that it is almost equal and then and then we are grouping the data based on mine and rock and we find their mean values and we get a quite difference in their mean values okay so as you can see here there is a difference in mean value of rock and mine for each of the column okay so now we are splitting the data into all the features and all on the labels so we are feeding all the features to the variable x and all the labels to the variable y okay now we are splitting our uh x and y our data into training and test data okay so the training data is used to train our model and our model is evaluated with the help of test data right and then we are loading our logistic regression model in the variable model and uh by the function model dot fit our model is trained it is just like a graph okay so in the x axis there will be this features under y-axis there will be labels okay and uh the graph will be plotted so this is how model is trained so once we have trained our model using the function model.fit we are finding the accuracy score so first we find the accuracy score of uh training data which is around 85 84 percentage and then we find the accuracy score for the test data which is around 76 percentage then we are making a predictive system where if we give the features if we give the data it can predict whether the object is a rock or mi okay so this is so these are these are all the procedures we have used in this use case so i hope you are clear with all these so i will be giving uh the link for the sonar data so the data file and also this follow-up file in the link of this link in the description of this video so you can download it from here so do try google collab so try to do all these things we have done here okay so try to write all this python script by your own and try to understand it so if you have any doubt if you if you run into any problems uh mentioned in the comment i will try to solve your problem okay so that&#39;s it from my side i hope you have understood the topic we are covered in this video so i&#39;ll see you in the next video thanks for watching

Transcript for:Rock vs. Mine Prediction Using Sonar Data

Transcript for:
Rock vs. Mine Prediction Using Sonar Data