Project 1: Prediction of Heart Disease : Project Overview
- Day by day the cases of heart diseases are increasing at a rapid rate.
- It’s very Important and concerning to predict any such diseases beforehand.
- Basing on the dataset , the analysis mainly focuses on which patient is more likely to have a heart disease based on various medical attributes.
- Prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient.
- Used different R programming libraries such as ggplot, corrplot, and Tidyverse which is R package for data science.
From the above Bar Chart we can observe the total number of patients having or not having Heart Disease. Conclusion form the above observation is that there are more cases of patients getting heart disease through the variable target.
These variable targets are Resting Blood Pressure, Chest Pain, Fasting Blood Sugar , sex and age group of the patients.
From the observation, many people are from the age of 55 to 60 are at a high risk of getting the heart disease compared to the younger ages.
Using Box Plot to compare Blood Pressure with Chest Pain on the basis of Sex.
from the above box plot we can see that the male experience the highest blood pressure when they get the ATYPICAL ANGINE chest pain compared to women
Project 2: Covid-19 Exploratory Analysis : Project Overview
- Perfomed exploratory analysis on Covid-19 from a dataset acquired from Kaggle
- Main aim was to relate different variables with one another to see the linear relationship they have
The above is a linear relationship
It shows how people have recovered comparing with the total number of cases. The more cases, ie 200000 cases over 80000 people have recovered.
On this scatter plot I use a hue effect
the hue parameter determines which column in the data frame should be used for colour encoding
The above plot shows the total_confirmed_cases as the independent variable, "hospitalized_with_symptoms" as the target variable and categorizing with the patients that have "recovered" which gives the hue.
From the above we can draw the conclusion that The number of people who had recovered is increasin at a rapid rate.
As a data analyst I can draw 3 major conclusions from the above analysis
- The people are increasing in recovering comparing to the number of total cases.
- We need to work on flattening the curve on the number of total cases, so that to reduce the number of patients in hospitals
- The number of deaths are also increasing with the total cases received.
Project 3: Covid-19 Exploratory Analysis : Project Overview
Using the above dataset to visualize the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a Stacked Line Plot or Area plot.
- Area plots are stacked by default
- To produce a stacked area plot, each column must be either all positive or all negative values
- (any NaN, i.e. not a number, values will default to 0). To produce an unstacked plot, set parameter stacked to value False.
