This project focuses on analyzing diabetes-related data and building predictive models using KNIME and Python. The main objectives of the project include:
- Data Analysis: Explore the relationships between diabetes and various health indicators such as age, blood pressure, BMI, glucose levels, etc. Analyze the dataset to uncover insights into the factors influencing diabetes outcomes using knime and python.
..............................................................................................................................................................
.............................................................................................................................................................. - Feature Generation: Create new features from the existing data to enhance the predictive power of the models. This includes generating intervals for age, blood pressure, and other relevant features to capture important patterns and relationships.
..............................................................................................................................................................
.............................................................................................................................................................. - Model Selection and Feature Selection: Evaluate different machine learning algorithms and feature selection techniques to identify the most effective models and features for predicting diabetes risk.
..............................................................................................................................................................
.............................................................................................................................................................. - Model Building: Build predictive models using the selected algorithms and features. Train the models on the dataset and assess their performance in terms of accuracy and other relevant metrics. ..............................................................................................................................................................
..............................................................................................................................................................
-
Data Analysis: Conducted a thorough analysis of the diabetes dataset to understand the relationships between various health indicators and diabetes outcomes. Explored correlations, distributions, and trends in the data to gain insights into the factors influencing diabetes risk.
-
Feature Generation: Implemented feature engineering techniques to create new features from the existing dataset. Generated intervals for age, blood pressure, BMI, etc., and encoded categorical features to enhance the predictive capabilities of the models.
-
Model Selection and Feature Selection: Explored a range of machine learning algorithms, including decision trees, random forests, support vector machines, etc., to select the best-performing models for predicting diabetes risk. Additionally, employed feature selection techniques such as recursive feature elimination to identify the most relevant features for model training.
-
Model Building: Built predictive models using the selected algorithms and features. Trained the models on the diabetes dataset and evaluated their performance using cross-validation techniques. Assessed model accuracy, precision, recall, and other metrics to determine the effectiveness of the models in predicting diabetes risk.
The project achieved the following results:
- Identified significant relationships between age, blood pressure, BMI, glucose levels, and diabetes outcomes through data analysis.
- Generated new features from the dataset, including intervals for age, blood pressure, etc., to improve model performance.
- Selected the most effective machine learning algorithms and features for predicting diabetes risk.
- Built predictive models with high accuracy and performance in predicting diabetes outcomes.
This project demonstrates the effectiveness of using KNIME for analyzing diabetes-related data and building predictive models. By conducting comprehensive data analysis, feature generation, model selection, and model building, valuable insights into diabetes risk factors were obtained, leading to accurate predictions of diabetes outcomes.