Predictive Health Risk Model Using Lifestyle Data
Budget: ₹750 – ₹1,250 INR
The “Predictive Health Risk Model Using Lifestyle Data” project is aimed at assessing potential health hazards like Diabetes, Asthma, Arthritis and Heart Attacks with the application of a few data mining and machine learning techniques. Here’s a breakdown of the project:
Objective:
The strategy is to make use of a volumetric data describing lifestyle in order to create a model which will predict in future weather a certain individual will contract a disease or not, by looking at changes and similarities in the individual’s lifestyle.
Data Source:
Kaggle Dataset: The lifestyle data used for this project comes from Kaggle. There might be other datasets which contain health and lifestyle variables including factors such as age, weight, food habits, exercises, smoking, drinking, sleep etc.
Data Processing & Storage:
Apache Spark: This type of software is primarily developed for dealing with extensive quantity of data in an efficient way. Apache Spark focuses on quick executes of operations even while processing streamed data at real time.
HDFS (Hadoop Distributed File System): HDFS is designed for the storage and organization of big data sets. It can be used to create a cost effective and distributed storage environment using several computers, which helps to speed up the retrieval of bulky data sets.
Feature Engineering:
Feature Selection & Transformation: Enhancing the model requires the use of relevant features which are first identified and finally selected from the dataset and then engineered. In particular, Body Mass Index (BMI), physical activity, smoking, and sleep duration are treated as important variables of disease risk.
Handling Missing Data & Outliers: Data cleaning encompasses removing outliers as well as filling in unrecorded values with programs to provide the dataset with quality and consistency.
ETL (Extract, Transform, Load) Operations:
Data are not only loaded but ETL operations including data cleaning and so on are performed using Python to prepare data for analysis. Data consists in extracting from different sources, transforming it by engineering relevant features after which the data is loaded into HDFS for storage and model learning.
Predictive Modeling:
Logistic Regression in PySpark: Based on historical data of known risk factors, we apply the Logistic Regression supervised machine learning method to classify people as being at risk of specific diseases. It is usually useful in targeting classification problems with two stationary states, as is the case with stable diseases.
The model accepts input containing various factors from the lifestyle of a person and predicts the odds of that person developing a specific illness within the individual's such as a Diabetes, Heart Attack.
Model Training & Evaluation:
- Training: The model learns from past data in the Kaggle dataset. It picks up on lifestyle habits and their links to health risks.
- Evaluation Metrics: The team uses standard metrics like Accuracy, Precision, Recall, and F1-Score to check how well the model works. They apply cross-validation methods to prevent overfitting and make sure the model handles new data well.
Visualization & Insights:
- Tableau Dashboards: After training the model and making predictions, the team puts the results into Tableau. This leads to interactive dashboards where users can look into different health risk factors, compare lifestyle choices, and see how these affect disease prediction.
- The dashboards offer an easy-to-use interface for viewing health risk assessments based on lifestyle inputs.
Real-Time Risk Assessment:
- Storing the data in HDFS and using Apache Spark to process it allows for quick health risk evaluations. This approach makes the model able to grow and handle big data flows in live settings.
Result:
The project produces a strong prediction model that uses lifestyle information to guess how likely different diseases are. It helps doctors, insurance companies, or people check and handle health risks by changing parts of their lifestyle that lead to diseases.
Skills & Tools Used:
- Big Data Tech: Apache Spark, HDFS
- Machine Learning: Logistic Regression Python, PySpark
- Data Pictures: Tableau
- Data Handling: Feature Building, ETL Jobs, Data Cleaning
This project shows skills in machine learning big data work making predictions, and showing data in pictures. It has real-world uses in healthcare and managing lifestyle risks.
Objective:
The strategy is to make use of a volumetric data describing lifestyle in order to create a model which will predict in future weather a certain individual will contract a disease or not, by looking at changes and similarities in the individual’s lifestyle.
Data Source:
Kaggle Dataset: The lifestyle data used for this project comes from Kaggle. There might be other datasets which contain health and lifestyle variables including factors such as age, weight, food habits, exercises, smoking, drinking, sleep etc.
Data Processing & Storage:
Apache Spark: This type of software is primarily developed for dealing with extensive quantity of data in an efficient way. Apache Spark focuses on quick executes of operations even while processing streamed data at real time.
HDFS (Hadoop Distributed File System): HDFS is designed for the storage and organization of big data sets. It can be used to create a cost effective and distributed storage environment using several computers, which helps to speed up the retrieval of bulky data sets.
Feature Engineering:
Feature Selection & Transformation: Enhancing the model requires the use of relevant features which are first identified and finally selected from the dataset and then engineered. In particular, Body Mass Index (BMI), physical activity, smoking, and sleep duration are treated as important variables of disease risk.
Handling Missing Data & Outliers: Data cleaning encompasses removing outliers as well as filling in unrecorded values with programs to provide the dataset with quality and consistency.
ETL (Extract, Transform, Load) Operations:
Data are not only loaded but ETL operations including data cleaning and so on are performed using Python to prepare data for analysis. Data consists in extracting from different sources, transforming it by engineering relevant features after which the data is loaded into HDFS for storage and model learning.
Predictive Modeling:
Logistic Regression in PySpark: Based on historical data of known risk factors, we apply the Logistic Regression supervised machine learning method to classify people as being at risk of specific diseases. It is usually useful in targeting classification problems with two stationary states, as is the case with stable diseases.
The model accepts input containing various factors from the lifestyle of a person and predicts the odds of that person developing a specific illness within the individual's such as a Diabetes, Heart Attack.
Model Training & Evaluation:
- Training: The model learns from past data in the Kaggle dataset. It picks up on lifestyle habits and their links to health risks.
- Evaluation Metrics: The team uses standard metrics like Accuracy, Precision, Recall, and F1-Score to check how well the model works. They apply cross-validation methods to prevent overfitting and make sure the model handles new data well.
Visualization & Insights:
- Tableau Dashboards: After training the model and making predictions, the team puts the results into Tableau. This leads to interactive dashboards where users can look into different health risk factors, compare lifestyle choices, and see how these affect disease prediction.
- The dashboards offer an easy-to-use interface for viewing health risk assessments based on lifestyle inputs.
Real-Time Risk Assessment:
- Storing the data in HDFS and using Apache Spark to process it allows for quick health risk evaluations. This approach makes the model able to grow and handle big data flows in live settings.
Result:
The project produces a strong prediction model that uses lifestyle information to guess how likely different diseases are. It helps doctors, insurance companies, or people check and handle health risks by changing parts of their lifestyle that lead to diseases.
Skills & Tools Used:
- Big Data Tech: Apache Spark, HDFS
- Machine Learning: Logistic Regression Python, PySpark
- Data Pictures: Tableau
- Data Handling: Feature Building, ETL Jobs, Data Cleaning
This project shows skills in machine learning big data work making predictions, and showing data in pictures. It has real-world uses in healthcare and managing lifestyle risks.