Ai ML project (predicting whether the person is sick or not )

We developed an artificial intelligence model based on machine learning techniques to predict an individual's health status (patient/non-patient) based on a set of medical characteristics such as age, blood pressure, blood sugar, and other vital signs.

Technical steps:

Data cleaning: Addressing missing values and ensuring data quality.

Exploratory analysis (EDA): Understanding the relationship between variables using graphs and descriptive statistics.

Data preprocessing: Normalizing data and splitting it into training and test data.

Model building: Using algorithms such as Logistic Regression, Random Forest, and SVM for prediction.

Performance evaluation: Calculating metrics such as Accuracy, Precision, Recall, and F1-score to ensure the model's accuracy.

Result: A predictive model helps support medical decisions by classifying patients quickly and effectively.

Tools and techniques: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn