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An introduction to machine learning and data preprocessing. It covers topics such as supervised and unsupervised learning, splitting datasets, regression, classification, clustering, and evaluating model performance. The document also includes steps for data preprocessing, such as importing libraries, importing datasets, checking for missing values, and feature scaling. Additionally, it discusses methods for rescaling, mean normalization, and standardization. code examples in Python using libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
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UNIT - II Statistics for Data Science SCHOOL OF mechanical ENGINEERING AND TECHNOLOGY
T. Y. BTECH UNIT III
Machine Learning Process Steps in Data Preprocessing
Step 2 : Import the Dataset
Step 3 : Check out the Missing Values
Step 4 : See the Categorical Values
Dummy Variables in Data Preprocessing
Step 5 : Splitting the data-set into Training and Test Set