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This repository documents my complete learning path—covering Python, NumPy, data preprocessing, statistics, supervised & unsupervised algorithms, model evaluation, and deployment. Building strong foundations and practical ML skills step by step
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57
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Merge pull request #23 from Stranger4uu/Day30_Function_transformer
cae0496View on GitHubstudied about Function transformer , like how they effect on our model and help for normal distribution
10cbef6View on GitHubMerge pull request #22 from Stranger4uu/Day29_ML_pipeline
cd3cf6fView on GitHubpracticed about complete pipeline in ml , using feature and also added train dataset too and my model pkl file
9e50b72View on GitHubadded 2 ipynb files in which i practiced what problems we will face if we don't use pipeline
5e6accfView on GitHubcreated a new folder named Model in Day 29 and added my model pkl files in it which are generated after creating my model on titanic data ( without pipeline )
43528e7View on GitHubsolved all problems/errors in my ipynb files, now there is no error
bb92100View on GitHubMerge pull request #20 from Stranger4uu/Day28_Column_tranformer
1b013e4View on GitHubadded covid_toy dataset and practiced Column_transform on that data
7e8f9aeView on GitHubadded a cars dataset and also practiced One hot endcoding ( Nominal encoding )
ec1dadaView on GitHubMerge pull request #18 from Stranger4uu/Day26_Categorical_Ordinal_Encoding
f49c416View on GitHubadded a dataset of customer and also practiced Ordinal encoding and Label endoing on Categorical Columns
d780987View on GitHubMerge pull request #17 from Stranger4uu/Day25_Feature_scaling_Normalization
101826dView on GitHub