Machine learning session 4
- 1. PROGRAMMING CRASH COURSE KEDS BIODESIGNS WEEK : II CLASS : 4 SESSION : MACHINE LEARNING DATA
- 2. INTRODUCTION While working with machine learning projects, usually we ignore two most important parts called mathematics and data. It is because, we know that ML is a data driven approach and our ML model will produce only as good or as bad results as the data we provided to it. In the previous chapter, we discussed how we can upload CSV data into our ML project, but it would be good to understand the data before uploading it. We can understand the data by two ways, with statistics and with visualization. In this chapter, with the help of following Python recipes, we are going to understand ML data with statistics.
- 3. LOOKING AT RAW DATA The very first recipe is for looking at your raw data. It is important to look at raw data because the insight we will get after looking at raw data will boost our chances to better pre-processing as well as handling of data for ML projects. Following is a Python script implemented by using head() function of Pandas DataFrame on Pima Indians diabetes dataset to look at the first 50 rows to get better understanding of it
- 5. UNIVARIATE PLOTS: UNDERSTANDING ATTRIBUTES INDEPENDENTLY The simplest type of visualization is single-variable or “univariate” visualization. With the help of univariate visualization, we can understand each attribute of our dataset independently. The following are some techniques in Python to implement univariate visualization.
- 6. MULTIVARIATE PLOTS: INTERACTION AMONG MULTIPLE VARIABLES Another type of visualization is multi-variable or “multivariate” visualization. With the help of multivariate visualization, we can understand interaction between multiple attributes of our dataset. The following are some techniques in Python to implement multivariate visualization
- 7. SCRIPT 2
- 8. RESULT