This repository is for anyone who would like to learn Scikit or Sklearn from scratch. I will be pushing all the code that I work and test on.I hope it will help.
Happy Learning!
The first file that your should refer to learn basics of sklearn is the "load_dataset.py" file. Through this file we undertsand that there are datasets within the sklearn library that we can use to learn and build algorithms.
The third step is to understand Data Preprocessing and use sklearn to perform data preprocessing.Data preprocessing is the crucial step of transforming raw data into a clean and usable format by handling missing values, removing duplicates, and normalizing features. This step enhances the quality and relevance of the data, thereby improving the performance of machine learning models.
One of the key concepts that come under working with an algorithm or a dataset are to be able to split it into test and train datasets or parts as that would help us in checking the accuracy of the algorithm or on a bigger term, the accuracy of the model. In the second python file, we learn how to use Sklearn to split our dataset. we use the train_test_split function to perform this task.