welcome back to part 5 of the bioinformatics project from scratch series where i show you how you could build your own computational drug discovery model using the machine learning algorithm in today's episode i will be showing you how you could compare several machine learning algorithms for building regression models of the acetylcholine esterase inhibitors and today we're going to be using a lazy and efficient way of building several machine learning algorithms and this was shown in a recent video using the lazy predict python library and so we're going to be using that for today's tutorial and
before proceeding further let's do a quick recap so in part one i have shown you how you could collect original data set in biology that you could use in your own data science project particularly i have demonstrated to you how you could download and pre-process the biological activity data from the chambo database and the dataset is comprised of compounds and molecules that have been biologically tested for their activity toward the target organism or protein of interest then in part two i have shown to you how you could calculate the lapinski descriptors which are descriptors used
for evaluating the likelihood of being a drug-like molecule and then i've shown to you how you could perform some basic exploratory data analysis on these lipinski descriptors particularly the eda are based on making simple box plot and scatter plot in order to visualize the differences of the active and inactive subset of the compound in part 3 i have made some changes to the target protein and then we're using the acetylcholine esterase as it provides a larger data set to work with and so in this part we have already computed the molecular descriptors using the paddle
descriptor software and then we prepared the data set comprising of the x and y data frames and then we use that to build a prediction model in the subsequent parts which is part four where we use the descriptors generated from part three in order to build a regression model using the random forest algorithm and now to today's episode let's get started so here we're going to be comparing several machine learning algorithms using the lazy predict library and so the first thing that you need to do is install the lazy predict and so in a prior
video i've shown you how you could use the lazy predict to do a quick and rapid model building of classification and also regression models in just a few lines of code and so let's start by installing the library okay and so we have already installed it and then we're going to be importing the necessary libraries and so here we're using the pandas seaborne and also the second learn library specifically we're importing the train test split function and then we're going to be importing the lazy predict and also the lazy regressor function and so now we're
going to be loading up the data set and we're going to be directly downloading it from the github of data professor and so the links is here wget to download it and now we're going to be reading in the file and then we're going to be assigning it to the df data frame then we're going to be splitting it up into the x and y variables and let's take a look at the dimension of the x variable and so here we see that it has a total of 4 695 rolls or the number of compounds
in the data set and it has a total of 881 descriptors or the features or the number of columns and so the first thing that we need to do is we're going to be removing the low variance features so those that have low variance and let's take a look at the dimension of the data set again and so we have a reduced subset from 881 to be 137 variables now we're going to be performing a data split using the 80 20 ratio all right now comes the fun part so as you can see here we're
going to be building more than 20 machine learning models and so we're using only two lines of code here so the first one is like any other scikit-learn functions for building the model is to assign the machine learning algorithm into a classifier variable and then we're going to be assigning the results from the prediction after we built the model and then we're assigning it to the train and test variables so the train and test variables here will be containing the performance of the model's prediction and so let's build the model so here it has 39
models 39 machine learning algorithms so this might take some time because the data is relatively big at almost 5 000 rows and so it should be noted here that model building is using default parameters for all of the 39 algorithms used and so if you want to perform hyper parameter optimization that will be a topic for another video right and so models have been built and let's have a look at the train okay so lg bm is the best model here so from our prior tutorials random forest was used for the model building and so
here it had slightly better performance let's have a look at the test set lgbm regressor random forest also at third place here but the thing is they're roughly the same okay 0.57 and 0.56 let's have a look at the data visualization of the model performance and so the bar plot of the r-squared values is provided here and we're going to have a look at the rmse values here and then we're also going to have look at the calculation time provided here so the longer the bars become the longer it takes to build the model all
right and so congratulations we have already built several machine learning models for comparison and if you're finding value in this video please give it a thumbs up subscribe if you haven't yet done so hit on the notification bell in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journey
