{"id":19225588,"url":"https://github.com/cano1998/daibetes-multiple-linear-regression","last_synced_at":"2026-05-13T13:45:36.861Z","repository":{"id":246062182,"uuid":"819988898","full_name":"Cano1998/Daibetes-multiple-linear-regression","owner":"Cano1998","description":"In this project I focused on applying multiple linear regression to analyze and interpret factors influencing diabetes outcomes. 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By interpreting the regression coefficients and assessing the model fit using the R² value, we gain insights into the factors that significantly impact diabetes.\n\nThe dataset includes various features related to patients' health and diabetes measurements, such as:\n\nAge\n\nBMI (Body Mass Index)\n\nBlood Pressure\n\nSerum Insulin\n\nBlood Glucose Levels\n\nDiabetes Pedigree Function\n\nOther relevant health indicators\n\n## Analysis and model\nData Preprocessing: Cleaning the data and preparing it for model training.\n\nExploratory Data Analysis: Understanding the distributions and relationships between variables.\n\nStandardizing the dataset using the StandardScaler method to ensure all features have a mean of 0 and a standard deviation of 1.\n\nMultiple Linear Regression: Building and fitting the regression model using multiple predictors.\n\nModel Interpretation: Interpreting the regression coefficients to understand the impact of each predictor.\n\nModel Evaluation: Using R² to assess the goodness-of-fit of the model.\n\n## Key findings\nModel 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.\n\nStandardization: All features were standardized using the StandardScaler method to ensure consistent scaling.\n\nSignificant Predictors: Identification of significant predictors that have a notable impact on diabetes outcomes.\n\nR² 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcano1998%2Fdaibetes-multiple-linear-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcano1998%2Fdaibetes-multiple-linear-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcano1998%2Fdaibetes-multiple-linear-regression/lists"}