Data Analysis is one of the most common processes to analyze the data and find out the answers to how and why things happened in the past. With this past data, we can create a machine learning module to predict future outcomes. In this video, we are going to create a Machine Learning model to predict diabetes. To predict the outcome we will cover the following process: 

1) Dataset importing: To perform analysis on data we need proper data. Here we will use the diabetes dataset and import this dataset into our code using NumPy. 

2) Data cleaning: Cleaning Dataset is one of the most important datasets for visualizing the data. We can handle missing values and some unused columns from the datasets. Ensure your data is correct and usable by identifying and removing any errors or corruption. It has the following characteristics - Monitor Errors, Validate Accuracy, Scrub for Duplicate Data, Delete all Formatting. 

3) Data visualization: Data Visualization is the presentation of data in graphical format. It helps to understand the significance of data by summarizing and presenting a huge amount of data. 

4) EDA on Diabetes Dataset: Exploratory data analysis is the approach of analyzing the critical process of performing an initial investigation of the dataset to discover patterns and anomalies, and form hypotheses based on our understanding of the datasets. It is used to analyze data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. 

5) Prepare Medicine Learning model: It is a mathematical representation of the output of the training process data, which can machine use this data to analyze the future outcome. Code - https://www.kaggle.com/akshitmadan/diabetes-eda-prediction/edit

Related Articles : https://www.geeksforgeeks.org/disease-prediction-using-machine-learning/