- This is the brief description of the dataset of the "heart.csv" and the "2015.csv"
- It tests the World Happiness Report and the Heart Problem to analyze the Classification and Regression Model
- It predicts the accuracy of the models by importing the different packages available, graph visualization and performing the preprocessing function on the dataset
- Thus, we can analyze them clearly using the various functions in Pandas, Numpy and Matplotlib
Heart Problem Dataset: https://www.kaggle.com/nareshbhat/health-care-data-set-on-heart-attack-possibility
World Happiness Dataset: https://www.kaggle.com/unsdsn/world-happiness
- The "heart.csv" contains 76 attributes where the column "target" refers to the presence of the heart disease in the patient where 0 refers to no or less chances of heart attack and 1 refers to high risk of heart attack
- The "2015.csv" contains the Happiness Report in the year 2015
- The Classification model uses the former dataset consisting of Age, Sex, Cholestrol, Blood Pressure, Fasting Blood Sugar, Maximum Heart Rate achieved
- The Regression model uses the latter dataset consisting of the Country with the Happiness Rank, Standard Error, Life Expectancy, Economy etc
- Perform the Classification and Regression Model using the Heart Problem and World Happiness (2015) datasets
- Calculate the accuracy of the modles so created by importing the various packages needed, visualizing them using graphs like histogram, bar chart etc
- I also downloaded the dataset and loaded it using Pandas and then removed all the null values from the dataset
- I also explored more commands and features on my own which helped me gain lots of insights
- Thus, I analysed the dataset provided to me given using relevant functions in Pandas, Numpy and Matplotlib
To further implement the further course of action to extend the functions of the Classification and Regression model that I was not able to implement the tasks that I was not able to develop in this task to enhance the performance of the task