Blood Donation Prediction using Machine Learning 💉🤖

Access here:https://blooddonationpredictionbelal.streamlit.app/

This repository contains code for predicting blood donation likelihood using machine learning models. The dataset used in this project (transfusion.data) contains information about individuals' blood donation history.

Steps Taken 📝

Data Loading and Preprocessing 📊

• Loaded the dataset from a CSV file (transfusion.data).
• Renamed the target column to "target".
• Checked the data types and basic information about the dataset.

Data Splitting 📂

• Split the dataset into training and testing sets using train_test_split() from sklearn.model_selection.

Model Training with TPOT 🚀

• Trained the TPOTClassifier model to find the best pipeline for predicting blood donation likelihood.
• Evaluated the model's performance on the testing data using ROC AUC score.

Log Transformation 📉

• Normalized the specified column ("Monetary (c.c. blood)") using log transformation.
• Checked the variance of the normalized data.

Model Training with Logistic Regression 📈

• Trained a logistic regression model using the normalized training data.
• Evaluated the logistic regression model's performance on the testing data using ROC AUC score.

Model Comparison 📊

• Compared the performance of the TPOT model and logistic regression model based on their AUC scores.

Model Serialization 📦

• Serialized the trained logistic regression model using pickle and saved it to a file (logistic_regression_model.pkl).
• Demonstrated loading the saved model from the file for future use.

Requirements 📋

• Python 3

Libraries 📚

• numpy
• pandas
• streamlit
• scikit-learn
• tpot