Decoding Hackathons | A Guide to the World Cup Hackathon | Geek Week 2023

Key Takeaways

[Music] [Music] [Music] e [Music] so hi everyone Ashi is the site and welcome to the session so the session is dedicated to as you can like see from the name itself decoding the hackathon I'm hoping like most of you have enrolled in the hackathon if you have not enrolled you can enroll it now as well this is going to be a data science and data analysis based hackathon where you will get a chance to work on a project like you will get an assignment you need to work on the haathon make that assignment make the project and submit it to us there are some important dates uh that you need to remember and the first is like the hackathon is already started so let me give you a brief about the hackathon then we will jump into how you can decode this hackathon how you can get the best out of this uh this hackathon how you can like maximum is your chance of winning and I'll share you my personal tricks as well that we are going to use like if you are having any uh like if you add these things in a project that will definitely add value in terms of evaluation when we are going to evaluate it so let me uh tell you about the hackathon first like what exactly is the hackathon for that purpose like this is the page which is available in the description as well so you can definitely check that out so this is a World Cup wizard so this is definitely totally dedicated to World Cup all the terms like and conditions and everything we are having any important dates how you need to submit everything is there on this page where you can see the start date the end date the location uh like it's online and the problem statement even the problem statement is also there you can click on here and then it will open the problem statement for you how everything works how the submission part Works what is the start date end date everything is there in the hackathon and at the end like you are also having uh like the judges including myself uh the criteria like the potential impact because it's definitely on uh uh cricket uh so because it's on Cricket World Cup so definitely you need to be in that particular range you need to work on that sector collecting the data making the dashboard comparing the player there are a lot of things that you can do and usage of the algorithm correct a technique that is definitely going plus u code quality complete the documentation everything will be there so we are going to discuss that everything as we proceed further into the course this uh means into this session and one very important thing we are having here make sure to make it interact up because I'm having like we are going to discuss about a lot about the pipeline so I'm going to have a lot of question for you so make sure to be a trend of there now uh as we are already discussing we are also having the price many as you can see here first price 7,000 second price 5,000 third price 3,000 and the next five with the gfg goodies and the next and free access to our courses okay so this is about the world of vard we are going to discuss in detail about the important aspects of it like any important dates at the end of the session but let me tell you like how this uh how we want the hack hackathon to proceed with if you are a beginner this is your first hackathon so or even if you are like experiences will you have worked on the hackathons so I'm going to tell you a brief about how the process will go right what are the two types of thing you can proceed further here with and I'm going to start with the name so the name of the this session is decoding the hackathon but I would love to call it as hack the hackathon because this session is totally dedicated to this how you can hack this hackathon right so as I told you there are two ways to proceed further with it because this is a cricet based analysis uh hackathon so we need to work on like the relevant data the relevant project so there are two ways to proceed further with it first is the data analysis one second is the data science one okay so data analysis as the name can suggest like we are going to mostly focus on data analysis part okay in data science as well a lot of things will be covered of data analysis and on top of that we are building machine learning and deep learning algorithm we are going to deploy them but in data analysis let's break the things to one by one let's break it to step by step process and then we'll see how the thing works okay hi a so next we are having so this is here you can see we are having six different pictures each different each picture we are having here represent one step of the data analysis project and let me Define how so just remember the pictures at this point of time the next one is the first picture so because it's the six-step process basically if I'm talking about data analysis it's a six step process First Step here is ideation what is the idea you are having for this world cup uh data that you are having you might compare uh the he of physique of different players you can make a UI where you can add like all the players of data you can like compare what are the runs they have made two player three paer four player you can compare them winning chances of the team comparison you can do uh so this is basically the data analysis just couple of use cases I have discussed you can do a hell lot of things on that okay so first step definitely is going to be the ideation how you're going to make the idea how the ideation process works okay once you have defined okay this is the idea I want to make uh let's suppose uh let's suppose uh a project which where you can select multiple players and you can compare them the based on the scores that they have done based on the Age based on the fitness based on the uh like matches they have played in the test and the OD in the World Cup any anything okay so if you want to make this so the next is a pipeline you need to define a pipeline in your mind how you will proceed further with the Cod uh with this hackathon okay now for uh defining the pipeline I mean to say how you will Source the data like how the actual output will look okay you are having the idea how you will Implement that idea for that purpose you need to make a pipeline okay this is also called as a project pipeline once you have defined the project pipeline okay this is the project want to make this is how you want to make it now you need to start searching for the next thing what is the next thing you are going to have make sure to add it on the chats okay okay A to Z motivation okay great nice presentation okay page so so if I'm talking about the data pipeline okay so in data pipeline what we are going to have we are going to basically have how the data will flow now in the next part what we are having we are having the data collection how you are going to collect the data so this is the library which represent basically the data set okay in terms of this particular project like this particular hackathon you can take the data set of matches you can take the data set of players you can take the data set of anything else like the stadiums like in which particular Stadium what what are the chances that India W like if there is a match of let's suppose India versus Pakistan Pakistan that's the like that that is celebrated like a festival in India and Pakistan as well so uh in these kind of matches like if you want to compare like in which particular Stadium what are the maximum ratio of Maximum chances of India to win or Pakistan to win right or Pakistan to lose probably so in these in these kind of cases like the uh the data of stadiums also need to be there so that's totally up to you how you are going to proceed further with this okay so next step is data collection for data collection there are lot of websites available for example kaggle is there a lot of online repositories are there like on GitHub like you can check if the data set is available a lot of websites are there from where you can scrape the data a lot of websites are there from where you can like call for the apis right you can also collect the data from there so there are a lot of uh data collection techniques that you can use or you can Al also use a data set which is already created sorted so what is the next step so you have made the plan you have the idea you have made the pipeline you have collected the data now the data is not always clean or means then you need to proceed proceed further with explor data analysis you're having the data set you have collected the data set by yourself using web scrapping or you or apis not upis so in that particular case what you will do uh in that particular you case you will have a better understanding how the data set is because you have collected the data set but let's suppose in some cases you have not collected the data set it's just already there you have downloaded the data set created by someone else and then you are using it you need to perform some explor data analysis on that even if it you have created it you also need to proceed further with explor data analysis you need to explore the data what are the maximum what are the minimums what are the number of players we are having what are the number of matches that are played what are the number of stadiums we are having right what is the fitness score on an average fitness score that a that a person should have that a player should have so this step called as explor data analysis you need to explore the data set right now once you have explored the data set the next step is data cleaning how you will clean the data set to clean the data set you need to for example like if there are some missing values there are a couple of Players whose data I don't know I don't know how many matches that they have played I am not having their Fitness code their weight their height let's suppose I'm not having it in the data set we are not having it in that particular case what we need to do we can just like we need to deal with those values either we can remove that row either I can remove the whole data of that particular player because if uh someone is using your website you have made a project to compare two players data any two player of your choice once for one player I want to compare let's suppose based on height right it is not a very important factor because it's not basketball but let's suppose at this point of time we are talking about the height let's suppose for player one I'm having height for player two I'm not having height right two player I'm comparing based on high height I want to compare it I'm not having the data of other players height so how I will compare it I'll not compare it I cannot compare it so that particular what I need to do same thing happen for other features as well so you need to know how data cleaning part should be done this step is very very crucial okay now once you have performed Eda once you have performed data cleaning now this is the time to analyze the data set to find important information from the data set okay and thus might tell you okay which player has scored most number of uh uh runs in ODI most number of runs in uh World Cup which team has won most number of World Cups right so these are the kind of comparison you can definitely do after analyzing and these are the analysis the analysis that you will get after that after analyzing everything you can make the graphs and you can showcase those graph on your websites some important aspect that like you some important Insight that you have got from this data set you've got from this data set you can definitely display it on your website okay so this is basically a six-step process coming up in the background we are having the same images ideation project pipeline data collection Eda stand for explorat data analysis if there is any like spelling mistake I'm making the correction while speaking only data cleaning and data analysis six step process if you're thinking this is it what do you think this is it or there is something else we need to do what we need to do next okay so the next step is uh this is definitely not the end because you have made the dash we have made the analysis now the next step is dashboard building we need to build the dashboard in some way right dashboard means you're having the analysis you need to host this analysis somewhere you need to make a website where people can select those two players right so you need to deploy those things this is a very crucial very important aspect that you need to cover must you must do it because otherwise you will be disqualified okay so the next step is as I've told you the last picture dashboard building and deployment you need to build the dashboard and you need to deploy it on the server so that anyone can use it for deployment you can use any server of your choice you can use flas Jango or any other framework of your choice but make sure your project is deployed so this is the first part that we have covered data analysis is covered now the next step is data science so in data science uh we need to complete all the things that you have done so far in data analysis on top of that you need to proceed further with couple of steps okay and what are those couple of steps those steps are these three okay so just make just remember these images we are going to discuss about each of it like all all those three in a in a minute okay so here you can see this step what is this this is not this is obviously not a black hole this is once we are having that data we need to basically make the pipeline in such a way as you can see here as well we need to P the pipeline such a way so that we can pre-process the data and we can feed it into the model because machine learning or deep learning U I'm not specific to machine learning algorithms only or deep learning algorithm totally up to you which algorithm works best suit best to your case okay so in this particular case what you need to do first of all you need to collect or basically pre-process the data in such a way so that you can feed it into the neural network or feed it into any machine learning algorithm of your choice that works based okay so this is a very important part Data pre-processing before feeding into any machine learning model and because we are making the pre-processing before feeding it to the neural network so the next step is obviously the neural uh not specifically neural network but any machine learning algorithm of your choice or deep learning algorithm so next step is building the model so in building the model as I've told you earlier as well totally up to you which model works best there's no one model that work best in all the cases if you are talking about deep learning algorithms it's not mandatory that all the Deep learning algorithms work best works better than machine learning algorithms it's not like that in some of the cases machine learning algorithm works even better than deep learning algorithms so you need to Choose Wisely which algorithm you need to use so this comes under model building part you are going to build the model once you have built the model then you need to check okay if the model is working perfectly or not what's the accuracy what is the F1 score what is the Precision what is the recall right so you need to evaluate the model how well your model is working and once you are done okay my model is working fine my model has got let's suppose uh like 90 more than 90% accuracy now I can deploy it okay now I've told you like these are the three steps on top of data analysis you need to do in data science mod uh data pre-processing in data pre-processing we are having all the things like splitting the data into train test validation so you can uh evaluate in the model building or model evaluation part so those are all the technique that you are going to use in the data pre-processing part but if I'm talking about these three steps data pre-processing model building and model evalution these are the mandatory things and on top of that because that's also not the end dashboard and deployment here dashboard I have written just to match the thing because earlier we were also using the dashboard but here dashboard means we need to make a proper UI we need to make a project so that anyone can use it and then you need to deploy it okay so these are the two step which are common okay so the first six steps of data analysis are common on top of that you can add the three move steps into it model building data pre-processing and evaluating the model and then you can deploy it in the same way here as well for deployment you can use any deployment technique of your choice any deployment server of your choice any deployment framework of your choice okay on top of that we are having some Ninja tips for you okay and those ninja tips are uh first one is complete the project okay don't leave the project behind if you are having like if you are adding five features into the project don't leave those tips behind and don't leave any feature behind if there is any incomplete feature that's just adding more burden to it if you want to make a great project great great project you need to remove a lot of good things from it same like if you want to write a great book you need to remove a lot of good chapters to make a great movie you need to remove a lot of good scenes same way just take only the great features in your project sort it the next is mean first is complete the project second is make the documentation anything you are doing make sure to have a proper documentation of this okay it will not only help you uh in this hackathon but it will also help you because you are making the project you might be collecting the data set from your own you are cleaning the data set you are making the machine learning model deploying and even deploying it so this is going to be an end to endend project this is this will you can treat it like an Capstone project if you put more efforts into it so you can definitely use this project in your resume you can share this project on a GitHub on Len in in the interviews you can def defitely add this project for that purpose you need to have a proper documentation of this project make sure to have documentation of it on top of that the documentation will also help you uh in the hackathon because we are also going to check how well you have documented this project so this is an NJ again make sure to have a proper documentation in the project any question any doubt you can add it in the chats okay now for uh deployment part like like streamate flas Jango fast API there are a lot of Frameworks that are there there are a couple of very easy framework for example flask on top of that like we are having flask u mean sorry streamlet then flask then Jango then uh fast AP so this is the order you need to make it in the reverse order right Jango fast API very scalable so make sure to use them like if you are not that much uh familiar into it you can start with flask and Jango as well but like this is the order go for either fast ajango then if not uh clask if not streamlet okay and there are some other Frameworks as well that you can use anyhow because this is the aak not only for data science but data science and web development because there are a lot of deployment part here as well next step UI will matter make sure to have a pretty decent UI make it simple make it crisp make it to the point UI is going to matter for sure two projects same effort even if like one project is better in terms of competition but the UI is the UI is the key because at the end you you are going to deploy this project to the end user there are millions of user just treat it like millions of user are going to use it just keep that thing in mind and then make it right read color theory like how the Color Works how the UI works okay make sure to have a good UI that will definitely like you will get some extra points from my side for sure if your UI is better okay now the next is definitely there are two ways to proceed further with this data analysis and data science data science is always a plus like I'll tell you the order deep learning is much better than machine learning machine learning is a bit better than data analysis right just the data analysis but that totally depend upon the use case how you're dealing with the things but if you are working on the machine learning or the Deep learning or U any other like models Advanced deep learning models so you will definitely get some extra points from our side but make sure to have a proper documentation don't write like in like call for the apas and then you have done the project and then you will be like okay I've done this it's not going to work like that okay the use case is definitely going to matter okay if you have made a pro a great project a proper use case of it you'll definitely get some extra points on that okay practical use case as I've told you like if there is any having a practical use case you will definitely get some extra points from our side hosting is mandatory hosting is mandatory if your website is not hosted you will be directly disqualified so make sure to host your website you need to share the host link of the website with us okay last but not the least which I have already discussed earlier as well which is don't complete uh don't leave anything incomplete complete the project don't leave anything incomplete okay so just to summarize everything like complete the project make the documentation make sure to use some good scalable deployment Frameworks UI will matter if you are having machine learning or deep learning or any Advanced Techniques using the project you will get some extra points practical use case will count hosting is mandatory and apart from that you need to submit the four things with us like in the submission part in the submission part you are going to write an article on based on your project that you have done so that is the mandatory part apart from that a demo video you are going to demonstrate your project how that project works so that it will be easier for us to understand and for for understand to us basically how the whole project and how the pipeline Works website link or the hosted link that you have made the project uh the hackathon website that you have made the UI the whatever project you have made you're going to share that with us so make sure to share the link of that that and the last thing is GitHub you are going to take all the project all the code that you have made make sure to host it on GitHub post it on GitHub and then you need to share that repository link as well make sure to have a proper documentation there on GitHub as well some important dates that are there on the page as well and still if anyone's having any question any anything so they can ask it so there are some important date which are given here as well starting date is 6th of October so that means it's already started end date is 15th of November 15th of November is the end date of submission of this hackathon so from 6th of October to 10th of November anyone can register even if you are not registered now you want to give it a try definitely give it a try you're going to learn a lot from this okay in the last Eaton we have seen students who don't know deployment while making the hackathon while making the project while working on the hackathon project they have learned how to deploy the things they have learned how machine learning works it will spe speed up your process of learning so make sure to give it a try even if you are not pretty great at machine learning or deep learning you know a bit of python data analysis make sure to give it a try you're going to learn a lot from this code uh of this haathon I can give you an assurance if you have started it you have given your 100% Till 15th of November okay I I can cannot guarantee you you will win the haathon but you are going to you will be multiple times better in this field in just like in just like it's almost a month not even a month 25 days okay so make sure to give it a try even if you are not great at it so from 6th of October to 10th of November anyone can register and if you made the project make you can make the submission earlier as well from 10th to 50th is the final date of submission okay okay 15th is the final 15th November is the final date of submission so make sure to submit it before that okay so these are I think uh the important dates and uh things we are having so this is pretty much it we I have given you the ninja tips as well so if you are having any question let me check if there is any question uh from your side as well thanks thanks a thanks a today motivation and facts thanks J and uh thanks saki for the presentation okay great so uh I think this is pretty much it and if anyone is having any question they can ask us in the chats otherwise like yes I think we can end the session so to summarize like two ways just to give a recap how everything works so we are going to start with the step one just to give it a brief in just two minutes okay let me take you to the first slid okay so hack Theon then data analysis ver versus data science you can either go for data analysis or data science to rest data analysis you need to start with data analysis uh to rest to data science part you need to complete the data analysis step for sure in data analysis six-step process and as we have discussed every step one by one so this is going to be the ideation then you will make a pipeline then you will collect the data then you will perform explor data analysis you will clean the data analyze the data and on top of that there are some common steps both in data science as well which is deployment dashboard building and deploying the project and then data analysis will be done data science will be definitely a a cherry on the cake if you are adding that you will get some extra marks for sure data science we are having an extra three-step process where you need to pre-process the data build the model and after building the model you need to check the accuracy of the model how well your model is performing and after that like data pre-processing model building model evaluation then again you will jump into the dashboard building and deployment and that's it and the tips I have shared with you the Q&A is also done and uh yes if you want to connect with me on Lon uh so you can search uh me on Google like ajra you can connect with me over there if I have having any question any doubt anything related to D you can connect with me over there so thank you so much everyone uh okay so Prav shast saying I'm 12 class wanting to participate what's the scoring criteria for the hackathon okay for the scoring uh criteria PR what you can do like you can go to the link which is given in the description there's everything is mentioned there's no like any scoring criteria anyone can participate to it and if you're 12th class student if you know python or data analysis a little bit you can definitely give it a try Okay so there's nothing like any scoring criteria you need to have for the winners so definitely there is a step-by-step process you need to follow like you like you need to go for the article L demo link website link GitHub link you need to deploy the project and then submit it to us and after that the valuation process will go on based on the evaluation based on the project the student have made based on the comparison you will get the marks and then the winners will be decided so so far there's no any selection criteria that we are having okay so thank you so much everyone for joining in shukria and uh if you are still having any doubt any question anything in your mind make sure to add it in the comments or you can connect with me over Linkin we can discuss there as well for the same so thank you so much everyone shukria bye