Data preprocessing and feature selection

Building predictive models

Model evaluation and comparison

Interpretation of model results and feature importance

Description: Predict the likelihood of diseases such as diabetes or heart disease using patient data.

Problem Understanding and Data Collection

Define the project goals and gather the dataset.

Brief overview and initial exploration of the dataset.

Data Preprocessing

Clean and preprocess the data.

Handle missing values, normalize$/$standardize features, and perform any nec$-$ essary transformations.

Exploratory Data Analysis $($EDA$)$

Conduct thorough EDA to understand data patterns and relationships.

Use visualization tools to highlight key insights.

Model Building

Select appropriate data mining techniques and build initial models

Experiment with different algorithms and parameters.

Model Evaluation

Evaluate model performance using relevant metrics.

Fine$-$tune models based on evaluation results.

Interpretation and Reporting

Interpret the results and extract actionable insights.

Prepare a detailed report summarizing the methodology, findings, and conclusions.

$[1]$ pip install ucimlrepo