As a data scientist we should be able to read different types of datasets. In this lesson, we will cover how to read dataset in different formats using Python and its various modules.
In this section, we will cover various methods that can help us to clean the dataset and make it suitable for a machine learning model. In general, we will cover the basic methods that every data scientist should know. We will learn about encoding, outliers, null values, and hundling imbalance datasets. By the end of this section, you will be comfortable to preprocess the dataset and make it clean.
Welcome to the first project!
This is going to be a very simple project which is about data analysis. Your task is to import the raw dataset and apply various methods to analyze and find the hidden trends from the dataset.
We have already provided the solution as well, but you are recommended to try the project first by yourself and then look at the attached file for the solution.
A machine learning is actually using some models to go through our dataset to find the trends and information from our data automatically. The machine learning can be superivsed or unsupervised. The supervised machine learning is when you have the target variable in our your dataset or you have a labeled dataset. The supervised machine learning will find the relation between the input data and the target variable and will use this relation to make prediction later.
In this section, we will go through various kinds of supervised machine learning models and will analyze them.
Create a data frame that has object values in more than one column. Now, how you can apply the Label Encoding method on multiple columns using a for loop?
Why One Hot Encoding cannot be applied on a target column?