Practical machine learning. Part 0.Prerequisite Basic knowledge about this is helpful Python NumPy Pandas Matplotlib Scikit-learn Part 1.Data Preprocessing Data preprocessing is an important step in the data mining process. Import the library. Get the data. Check for missing or null data. Convert categorical data into numbers. Split into train and test. Feature scaling. For data preprocessing use this Jupyter notebook Part 2.Supervised Learning Supervised learning is the learning of the model with an input variable and an output variable and algorithm map the input to the output. Supervised learning classified into two categories of algorithms: Classification : A classification problem is when the output variable is a category, such as "disease" or "No disease". Regression : A regression problem is when the output variable is a real value, such as "Price". 2.1 Classification There is a wide variety of classification applications from Healthcare to M
