Day 42 (22) of ML
> Feature Engineering
> Started with Feature Scaling.
> Worked on Standardization also called Z-score Normalisation.
> Things to learn:-
• Missing Values Imputation
• Handling Categorical data
• Outlier Detection
• Feature Scaling
• Feature Construction
• Feature Selection
• Feature Extraction
#buildinpublic
Day 41 (21) of ML
> Understanding Data
> Worked with EDA on Bivariate data and Multivariate data.
> Worked on Pandas Profiling.
> Pandas Profiling Reports :- Pandas Profiling Report (now commonly called YData Profiling) is an automated Exploratory Data Analysis (EDA) tool that generates a detailed report of a dataset with a single command.
#buildinpublic
Day 42 (22) of ML
> Feature Engineering
> Started with Feature Scaling.
> Worked on Standardization also called Z-score Normalisation.
> Things to learn:-
• Missing Values Imputation
• Handling Categorical data
• Outlier Detection
• Feature Scaling
• Feature Construction
• Feature Selection
• Feature Extraction
#buildinpublic
Day 41 (21) of ML
> Understanding Data
> Worked with EDA on Bivariate data and Multivariate data.
> Worked on Pandas Profiling.
> Pandas Profiling Reports :- Pandas Profiling Report (now commonly called YData Profiling) is an automated Exploratory Data Analysis (EDA) tool that generates a detailed report of a dataset with a single command.
#buildinpublic
Day 40 (20) of ML
> Understanding Data
> Worked on Exploratory Data Analysis (EDA) on Univariate Data.
• Univariate Data - When you work on a Single Particular column as variable individually.
• Worked with both Categorical and Numerical data.
#buildinpublic
Day 40 (20) of ML
> Understanding Data
> Worked on Exploratory Data Analysis (EDA) on Univariate Data.
• Univariate Data - When you work on a Single Particular column as variable individually.
• Worked with both Categorical and Numerical data.
#buildinpublic
Day 39 (19) of ML
Couldn't work much for being busy in college works.
This shitty ass college provides mails which takes 40-50mins to fill forms and understand the mails.
But how managed to revise the previous ML stuffs.
#buildinpublic
Day 39 (19) of ML
Couldn't work much for being busy in college works.
This shitty ass college provides mails which takes 40-50mins to fill forms and understand the mails.
But how managed to revise the previous ML stuffs.
#buildinpublic
Day 38 (18) of ML
• Done with the BIG 3
Today's revision:
• Histograms
• Pie charts (single & multiple)
• Working with multiple graphs in the same figure
• Pie charts using MultiIndex DataFrames
Some days you don't crack anything new.
You just get a little smoother with the tools you already have — fewer "wait what does this even mean" moments, faster instincts.
That's a good day too, honestly.
Anyone else count that as a W or is it just me?
Day 38 (18) of ML
• Done with the BIG 3
Today's revision:
• Histograms
• Pie charts (single & multiple)
• Working with multiple graphs in the same figure
• Pie charts using MultiIndex DataFrames
Some days you don't crack anything new.
You just get a little smoother with the tools you already have — fewer "wait what does this even mean" moments, faster instincts.
That's a good day too, honestly.
Anyone else count that as a W or is it just me?