The objective of this project is to build a multiple linear regression model to understand the relationship between various predictors and diabetes outcomes. By interpreting the regression coefficients and assessing the model fit using the R² value, we gain insights into the factors that significantly impact diabetes.
The dataset includes various features related to patients' health and diabetes measurements, such as:
Age
BMI (Body Mass Index)
Blood Pressure
Serum Insulin
Blood Glucose Levels
Diabetes Pedigree Function
Other relevant health indicators
Data Preprocessing: Cleaning the data and preparing it for model training.
Exploratory Data Analysis: Understanding the distributions and relationships between variables.
Standardizing the dataset using the StandardScaler method to ensure all features have a mean of 0 and a standard deviation of 1.
Multiple Linear Regression: Building and fitting the regression model using multiple predictors.
Model Interpretation: Interpreting the regression coefficients to understand the impact of each predictor.
Model Evaluation: Using R² to assess the goodness-of-fit of the model.
Model Coefficients: Each coefficient in the regression model represents the change in the diabetes outcome for a one-unit change in the predictor, holding other predictors constant.
Standardization: All features were standardized using the StandardScaler method to ensure consistent scaling.
Significant Predictors: Identification of significant predictors that have a notable impact on diabetes outcomes.
R² Value: The R² value indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. A higher R² value suggests a better fit of the model to the observations.