🚀 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.

📌 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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[gloomyday]

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!🙂

[vrishabhbhonde]

Thanks a lot sir. Very helpful and very clear steps

[percidaman]

Thanks man this was so great, you really helped me

[mitchellyula]

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

[AmahaGebretsadikan]

I like it the organisation and contents of the presentation

[yasink]

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

[melissameeker]

Thank you so much you helped me understand

[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!

[bhaskarmondal]

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

[alfredturkson]

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

[AB]

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!

[rekhamalik]

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

[akhandsingh]

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

[yasinimudy]

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

[khushboo]

Is there any video of machine learning model of this data

[mohitjoshi]

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

[gayathrikrishnamoorty]

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

[bhushansonawane]

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