🚀 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/datasets/kumar...
📌 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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[@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!

[@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

[@vrishabhbhonde6899]

Thanks a lot sir. Very helpful and very clear steps

[@percidaman4409]

Thanks man this was so great, you really helped me

[@kiruthickagp]

Very clearly explained

[@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.

[@anurag17091977]

stupendous video. keep it up bro.

[@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!

[@melissameeker3189]

Thank you so much you helped me understand

[@nabinbk1065]

thank you sir. you are great

[@hiteshsharma8368]

Nice vedio thanks brother ❤

[@alfredturkson1319]

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

[@Akash-us3mo]

Thankyou

[@Balaji-wb7cp]

Superb bro

[@rekhamalik3663]

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

[@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

[@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?

[@yasinimudy8688]

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

[@raghavendraraodk7855]

Sooper

[@mohitjoshi8984]

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

[@maskedvillainai]

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

[@gayathrikrishnamoorty4243]

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

[@muhammadsamir2243]

Please share the notebook link

[@cryptofile4002]

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

[@amanagrawal1976]

Pls provide jupyter notebook code

[@ayushjaiswal350]

okay video

[@bhushansonawane5915]

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

[@devanshupatnaik_video6387]

Is this is data cleaning method??

[@user-yk9zr4ud5q]

Normalization?

[@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.

[@nguyenthiyenhuong2344]

where is Normalization? pls

[@davidprayogo3944]

adding code script to next time, please

[@user-pu7ye8lu3c]

WORTH VARMA WORTH

[@lilaclove1709]

🙂

[@prabhatkumar-0145]

provide a csv file also

[@learnwithankit383]

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

[@mayfield7835]

700th like

[@bevg1]

slow down a bit...