Part 1 Report
Part CLO3
In this project, the most appropriate methodology to use would be the CRISP-DM
(Cross Industry Standard Process for Data Mining) methodology. The CRISP-DM
methodology is widely used and provides a structured approach to data mining
projects, consisting of six phases:
● Business Understanding: In this phase, we define the project's goals and
objectives and assess how the results will be used to benefit the organization.
We also identify the stakeholders and their requirements.
● Data Understanding: In this phase, we collect and analyze the data to gain an
understanding of its structure and quality. We also identify any missing or
erroneous data.
● Data Preparation: In this phase, we clean, transform, and integrate the data to
prepare it for analysis.
● Modeling: In this phase, we select and build a suitable model for the problem at
hand. We also evaluate the model's performance and make any necessary
adjustments.
● Evaluation: In this phase, we assess the model's performance on a holdout
dataset to determine its effectiveness and identify any areas for improvement.
● Deployment: In this phase, we deploy the model in the production environment
and monitor its performance to ensure it continues to meet the organization's
needs.
The CRISP-DM methodology is suitable for this project because it provides a structured
approach to data mining projects, ensuring that all aspects of the project are considered
and addressed. It is also a flexible methodology that can be adapted to suit different
types of data mining projects. Furthermore, CRISP-DM is widely accepted and
recognized, making it a well-established standard in the data mining industry. By using
CRISP-DM, the General Hospital can be confident that they are following a proven
methodology for developing their heart attack prediction system.
Part CLO2
The Cleveland Heart Disease dataset is a well-known dataset in the field of heart
disease diagnosis and prediction. It contains 303 records of patients with various
attributes related to heart disease, such as age, sex, cholesterol levels, chest pain type,
and ECG measurements.
The goal of the dataset is to predict the presence of heart disease in patients based on
their attributes. The target variable is a binary variable indicating whether a patient has
heart disease or not. This dataset has been widely used in research and machine
learning models development related to heart disease prediction.
The Cleveland Heart Disease dataset is a valuable resource for researchers and medical
professionals in the field of cardiology. It has contributed significantly to the
development of various heart disease prediction models and the understanding of the
factors that contribute to heart disease.
Part CLO5 Q3
here are the tasks that can be applied to each phase of the CRISP-DM methodology for
the Cleveland Heart Disease dataset, along with the reason for each task:
Define the problem: Define the problem that we are trying to solve with the heart
disease prediction system. The reason for this task is to establish the objectives of the
project and to ensure that we are addressing the key requirements of the stakeholders.
Identify the goals: Identify the specific goals and success criteria for the project. The
reason for this task is to ensure that the project is aligned with the organization's
strategic objectives.
Collect data: Collect the data related to the heart disease diagnosis and prediction from
various sources, including the Cleveland Heart Disease dataset. The reason for this task
is to ensure that we have the data needed to build a robust heart disease prediction
model.
Explore data: Explore the data to understand its structure, quality, and completeness.
The reason for this task is to identify any data quality issues that need to be addressed
before building the model.
Verify data quality: Verify the quality of the data by checking for missing values, outliers,
and inconsistencies. The reason for this task is to ensure that the data is accurate and
reliable.
Clean data and Transform data: Clean the data by removing missing values, duplicates,
and outliers. The reason for this task is to ensure that the data is ready for analysis and
modeling.Transform the data by normalizing or scaling the features, converting
categorical variables to numeric, and creating new features if necessary.
Select modeling technique: Select a suitable modeling technique, such as logistic
regression, decision trees, or support vector machines, for building the heart disease
prediction model. The reason for this task is to identify the best algorithm that can
accurately predict the presence of heart disease in patients.
Build model: Build the heart disease prediction model using the selected algorithm and
the preprocessed data. The reason for this task is to develop the model that can predict
the presence of heart disease in new patients accurately.
Evaluate model: Evaluate the performance of the model using relevant metrics such as
accuracy, precision, recall, and F1-score. The reason for this task is to determine the
model's effectiveness in predicting the presence of heart disease in patients
Deploy model: Deploy the heart disease prediction model in the production
environment, where it can be used to predict the presence of heart disease in new
patients. The reason for this task is to make the model available for use by medical
professionals.
Part CLO5 Q4-Q5
There are several data mining modeling techniques that can be applied to the Cleveland
Heart Disease dataset to help General Hospital build its future heart disease prediction
system. Here are a few examples:
Logistic Regression: Logistic regression is a popular modeling technique used to
predict binary outcomes, such as the presence or absence of heart disease. It can help
General Hospital build a model that predicts the probability of heart disease based on
patient characteristics, such as age, sex, cholesterol levels, and blood pressure.
Decision Trees: Decision trees are a modeling technique that can help General Hospital
build a model that predicts the presence of heart disease based on a series of
questions or criteria. For example, the model might ask if the patient is over 50 years
old, if they have high cholesterol, and if they smoke. Based on the patient's answers, the
model can predict the probability of heart disease.
Support Vector Machines: Support vector machines (SVMs) are a modeling technique
that can help General Hospital build a model that predicts the presence of heart disease
based on patient characteristics. SVMs work by finding a hyperplane that separates the
data into two classes, in this case, patients with and without heart disease.
Neural Networks: Neural networks are a type of machine learning algorithm that can
help General Hospital build a model that predicts the presence of heart disease based
on patient characteristics. Neural networks work by simulating the structure and
function of the human brain, using layers of interconnected nodes to learn patterns in
the data.
Overall, the choice of modeling technique will depend on the specific requirements of
the General Hospital's heart disease prediction system and the nature of the data in the
Cleveland Heart Disease dataset. It may be necessary to experiment with multiple
modeling techniques to find the one that provides the best results.
Part CLO5 Rapid Miner Project
1. The following data preprocessing steps may be needed ● Remove duplicate and missing data values.
● Normalize the data
● Split the data into training and testing
2. On the basis of accuracy, the decision tree is the best
classification technique.
3. The best forecasting model is the decision tree when
compared with random Forest , naive bayes in real time.
The model should take minimum time for forecasting
purposes.
4. The following evaluation parameters calculated from the
decision tree model in Rapid Miner -
5. The confusion Matrix obtained for the decision tree model.
6. The following represents the decision tree model in rapid
miner -