sir please also make a book of advance eda and data pre pocessing please

great sir g super

I learned about Data Transformation. --> These are 3 Scalars for Linear Data # Standardization or mean Removal: 1:Standard Scalar [-3,3] (used for models which deal with -ve values as well) # Scaling to a known range: 2: Min-Max Scaling [1,0] (used for models which deal with +ve values only) 3: Max-Absolute Scaling [-1,1] (used for models which deal with -ve values as well) --> If the data has non-parametric distribution i.e. the data is not normally distributed, then we use Quantile Transformer.We also use Box-Cox, Yeo-Johnson, Quantile transformers.

Done

Done

I learned in this lecture 1: Standard Scalar ( -3 —------- +3 ) ( handle negative value ) 2: Min-Max Scalar ( 0 —--------1) 3: Max-Absolute Scalar ( -1 —-------- +1 ).

I learned 1: Standard Scalar ( -3 —------- +3 ) ( handle negative value ) 2: Min-Max Scalar ( 0 —--------1) 3: Max-Absolute Scalar ( -1 —-------- +1 ).

AOA, I learned in this lecture about transformation and its types. TYPES OF TRANSFORMATION1: Standard Scalar ( -3 —------- +3 ) ( handle negative value ) 2: Min-Max Scalar ( 0 —--------1) 3: Max-Absolute Scalar ( -1 —-------- +1 ) ( handle negative value )I also learned about non-parametric distribution, which is a QUANTILE transformation (converting the data in uniform), and another one is MAPPING (converting the data to a Gaussian distribution). ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey Ameen.

I learned standardization steps (i.e. standard scaling, min-max scaling, and max-abs scaling). If algorithms deal with negative values then we can use standard and max-abs scaling otherwise min-max scaling