Briefly describe the purpose and goals of your diabetes prediction project.
diabetes_prediction_project/ |-- data/ | |-- raw/ | |-- diabetes_data.csv # Raw data file | |-- processed/ | |-- cleaned_data.csv # Processed and cleaned data |-- notebooks/ | |-- exploratory_data_analysis.ipynb # Jupyter notebook for data exploration | |-- feature_engineering.ipynb # Jupyter notebook for feature engineering |-- src/ | |-- init.py | |-- data_preprocessing.py # Module for data cleaning and preprocessing | |-- feature_engineering.py # Module for feature engineering | |-- model_training.py # Module for training the diabetes prediction model | |-- model_evaluation.py # Module for evaluating the model |-- models/ | |-- trained_model.pkl # Saved trained model file |-- requirements.txt # List of dependencies for the project |-- config.yaml # Configuration file for hyperparameters, settings, etc. |-- scripts/ | |-- run_model_training.py # Script to run the model training process | |-- run_model_evaluation.py # Script to run model evaluation |-- README.md # Project documentation |-- LICENSE # License file
Data set can be downloaded from kaggle .
Load data from the markdown file. Explore and understand the data. Handle missing values. Encode categorical variables. Scale numerical features. Perform feature engineering if needed. Handle outliers. Split the data into training and testing sets. Address imbalanced data if applicable. Normalize data if necessary. Ensure data is in the right format for the model. Save preprocessing information for deployment.
Share insights gained from exploratory data analysis. Use visualizations and summary statistics to highlight key patterns or trends in the data.
Describe the machine learning model(s) used for diabetes prediction. Include information about the algorithm, hyperparameters, and any other relevant details. If applicable, discuss the model selection process.
Present the evaluation metrics used to assess the performance of your model(s). Include accuracy, precision, recall, F1-score, and any other relevant metrics. Discuss the results and any potential areas for improvement.
Provide contact on tanishjain5903@gmail.com for questions or feedback.