🚀 Data Cleaning/Data Preprocessing Before Building a Model - A Comprehensive Guide

Welcome to Learn_with_Ankith! 📊 In this tutorial, we'll delve into the crucial steps of data preprocessing to ensure your datasets are in prime condition before feeding them into your machine learning models. A clean and well-prepared dataset is the foundation for accurate and reliable model predictions.
Data_set link: www.kaggle.com...
📌 Topics Covered:
🚀 Data Cleaning/Data Preprocessing Before Building a Model - A Comprehensive Guide
Import Necessary Libraries: Learn the essential libraries required for efficient data manipulation and analysis.
Read File: Understand how to import data from various sources and formats into your Python environment.
Sanity Check:
Identify and handle missing values effectively.
Explore the dataset's shape, information, and spot duplicates.
Conduct a garbage check to maintain data integrity.
Exploratory Data Analysis (EDA):
Dive into descriptive statistics for a deeper understanding of your data.
Visualize data distributions with histograms and box plots.
Uncover patterns and relationships with scatter plots and correlation heatmaps.
Missing Value Treatment:
Implement strategies using mode, median, and KNNImputer to handle missing data.
Outlier Treatment:
Explore methods to detect and deal with outliers that can impact model performance.
Encoding of Data:
Convert categorical variables into a format suitable for machine learning algorithms.
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[@chimdiihenacho4985]

This is the best tutorial I have come across as a machine learning student. This has given me the entry I needed to get shit done.. Thanks a lot Ankith

[@gloomyday4524]

you dont know how much this video help clueless students like me, you did such a good thing bro, i hope everything will always goes easy in your life!

[@mustaphakwari4536]

I am truly at a loss for words to express the value of this tutorial. It is incredibly insightful, educational, and highly informative. A perfect roadmap for beginners. My sincere appreciation to the presenter for such a fantastic session!

[@ContehAlimamy-p1k]

you forgot one step step 8: Normalization. who else notice in the video. Thank you so much for the video.

[@amina._.1862]

I like the layout, very professional and shows exactly each process (what it is) step by step tysm

[@kiruthickagp]

Very clearly explained

[@nithyarajan7317]

So much details & good explanation sir .. Thank you so much for the video

[@bhaskarmondal7461]

Thank you so much Sir,
For providing this particular Kind of tutorial!, which is specifically targeted for Machine Learning rather than Data Analysis. Also, I was looking for something just like this for last few days

[@learnwithankit383]

"Great to hear that you found the tutorial helpful! "

[@bhaskarmondal7461]

Again, Thank you for your efforts :) @@learnwithankit383

[@AmahaGebretsadikan]

I like it the organisation and contents of the presentation

[@mitchellyula4447]

Thank you for this walkthrough. This will help me on my next project for school.

[@bombasticiti]

Nice, Thank you for feeding my mind!🙂

[@yasink18]

Thank you so much for making simple video ..
Can you make more video on just handling different outliers type and how to understand only what type of outliers we need to handle or ignore

[@AB51002]

Could you also make a video exploring and cleaning text data? Something like what LLMs train on, but obviously much smaller. Something like 1GB of text perhaps. I can't find any online resources targeting that specifically, and it could help many people learn how to better filter text dataset for higher quality datasets. Thank you in advance!

[@kartikgupta8413]

did you find something like that?

[@Akash-us3mo]

Thankyou

[@FrancisNzubechukwu-qe6mj]

Thanks for the free lesson💌

[@Vaishnavi31-b4z]

Awesome tutorial bro!! Thanks!!

[@stefan5249]

Hi, well structured turtorial. Systematicallly for understanding what to do in a first data inspection. Thank you!

[@rekharekha-ll4cp]

Excellent explanation, now only i understood the preprocessing

[@SonaliDey-e5u]

Thanks bro for your informative video. This video saved me from such a mess which I was not able to understand

[@Dadepegba]

I love your lesson, you explain very clearly. Thank you.

[@kelvinau3606]

Thanks for the video, brother, love it

[@anurag17091977]

stupendous video. keep it up bro.

[@vrishabhbhonde6899]

Thanks a lot sir. Very helpful and very clear steps

[@minalgupta7456]

nice explanation

[@thaisbraz9092]

Thank you so much for this video❤

[@ayshafida3413]

Superb video

[@Ciiads]

good job👌👌❤❤

[@percidaman4409]

Thanks man this was so great, you really helped me

[@AmaRan31]

Thanks a lot for this video!

[@hey_hae]

very clear explanation thank u！

[@Shahzaib_Aqeel]

@Ankith Kindly Share the notebook as well please.

[@umerahmed7062]

while filling the missing values you also filled the life expectancy you previously said that the Life expectancy shouldn't be touched etc . I think you have performed the work which you said to avoid

[@mukhammedabusuveilim3468]

Overall very a good video. Would've been great if you add specific section for continuous and categorical data types. Another point, I don't understand why you showed the correlation matrix if you didn't use it to filter out highly correlated features (there a couple that were fully correlated and I assume some that were highly correlated).

[@learnwithankit383]

Thanks for the feedback. While this video focused on the initial cleaning steps, the correlation matrix is often crucial for feature selection.

[@vaibhavchaganti1709]

sir why you Missed Normalization ? (step 8)??

[@fasiowaizahmed4641]

great video !!

[@alfredturkson1319]

How did you set up your jupyter notebook? the settings to make mine look like yours please

[@AtomicPixels]

You can skip literally every step here by uploading your data to hugging face and opening the auto train data viewer tool that’s auto generated for you. It includes the answers to all of these problems already with no code or time spent making it a task you don’t need to be focused on

[@adritaadi8027]

can i do this on kaggle? following the same steps?

[@Soumyadeep_Chakraborty1]

Yes

[@melissameeker3189]

Thank you so much you helped me understand

[@abdulbasithassan1742]

good brother

[@onlyguitars]

Hi! Great video, very helpful and love how each step is clearly outlined! Just a question. In the outliers why change the value to the UW and LW, and not just drop those rows? Thank you!

[@SalmanKhan-e9u3e]

Thank you so much sir

[@mr.malluclasher]

sir.. do i need to fix the skewness before encoding and scaling?

[@priyankakasturia]

Can I use interpolation instead of mean or median if I have time series data with missing numeric values?

[@minalgupta7456]

upload more projects related to the data scientist

[@Mission_Satyuga]

Nice vedio thanks brother ❤

[@nabinbk1065]

thank you sir. you are great

[@md.sowmik7944]

Best

[@khushboo4743]

Is there any video of machine learning model of this data

[@aliloreno]

2:39 imports

[@akhandsingh6497]

Thanks for this video and I want to ask you that how you can get run time in Jupiter notebook pl tell me

[@Balaji-wb7cp]

Superb bro

[@roshanbhattad4493]

Hi Ankith, thanks for the turorial. I do have a question can we do missing value treatment before EDA ?

[@Soumyadeep_Chakraborty1]

Yes we can

[@mohitjoshi8984]

Hello
Help in correlation part it showing NaN and 0.0
Please help

[@rekhamalik3663]

Amazing!
Can you please make video with complex json files i.e stock market data?

[@RajenderKumarG]

at 34:30 BMI is not working. After replacing inplace = True by false and removing BMI, it is working. Please help

[@LaxmiKumari-ru8lu]

Same.. Have you found any solution of this problem?

[@asma5202]

add a space befor and after , like this " BMI "

[@yasinimudy8688]

Nice video, however I would like if ".fit_transform" method of KNNImputer does not cause data leakage when applied to fill null values.

[@muhammadsamir2243]

Please share the notebook link

[@gayathrikrishnamoorty4243]

what will we do if we find duplicates in dataset??

[@raghavendraraodk7855]

Sooper

[@devanshupatnaik_video6387]

Is this is data cleaning method??

[@pra438]

Please provide notes also

[@bhushansonawane5915]

Hello sir, how can i connect with you ? Need urgent help please

[@cryptofile4002]

@Learn with Ankith can you pls offer the code for this?

[@DivyaChindam-h3p]

Normalization?

[@jglez6868]

I want to add something.. when you are dealing with missing values, lets say for the polio column. You should replace those value for the mean of polio of the corresponding country, if you do the mean overall you might get a slightly different value then let say find the mean of polio in Yemen and replace it by it.. So its always good to think of ways to not generalize much and replace by more specific realistic data

[@amanagrawal1976]

Pls provide jupyter notebook code

[@shanthalaxmikumar4931]

Thank u for this ,,, can you please tell us in case of date data?

[@nguyenthiyenhuong2344]

where is Normalization? pls

[@ayushjaiswal350]

okay video

[@mayfield7835]

700th like

[@poiuytrewq-d8e]

WORTH VARMA WORTH

[@prabhatkumar-0145]

provide a csv file also

[@learnwithankit383]

www.kaggle.com/datasets/kumarajarshi/life-expectancy-who

[@iizrael]

Please how can I install pandas and the rest to my notebook because mine is showing me error if I try importing as you did yours

[@learnwithankit383]

Try to execute : !pip install pandas in Jupyter Notebook.

[@lilaclove1709]

🙂

[@bevg1]

slow down a bit...

[@iShowSweat-iest]

adding code script to next time, please

[@jesuslopez6873]

always an Indian...