Diabetes Prediction System

Key Features:

• Data Preprocessing: Handles missing values, scales data, and prepares it for training.
• Model Training: Trains a logistic regression model using training data.
• Prediction: Based on the input, predicts whether the person has diabetes or not.
• Model Evaluation: Evaluates the model’s performance using accuracy and confusion matrix.

🛠️ Tools & Libraries Used

• Python: For implementing the logic and model.
• Scikit-learn: For creating and evaluating the machine learning model.
• Pandas: For data manipulation and preprocessing.
• Matplotlib: For data visualization.
• NumPy: For numerical operations and array manipulation.

💡 Learning Outcomes

• Learned how to preprocess data and handle missing values.
• Gained experience with machine learning algorithms, specifically logistic regression.
• Improved skills in evaluating models and understanding performance metrics like accuracy and confusion matrix.

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Student Management System

🚀 Key Features

• Add Student: Insert new student data into the system.
• Update Student: Modify existing student records.
• Delete Student: Remove student data permanently from the database.
• Search Student: Search for specific students using various filters like ID, Name, Phone, Email, etc.
• Export Data: Export all student data to a CSV file for easy record-keeping.
• Database Integration: Utilizes MySQL for backend data storage.
• Data Validation: Ensures that all fields are correctly filled and prevents duplicate IDs.
• User-Friendly Interface: Built with Tkinter for an intuitive GUI experience.

🛠️ Tools & Libraries Used

• Python (Tkinter): For building the graphical user interface.
• MySQL (pymysql): For database connectivity and operations.
• Pandas: For exporting data to CSV files.
• ttkthemes: For styling Tkinter windows.
• Messagebox & Filedialog: For user interactions and file handling.

💡 Learning Outcomes

• Gained hands-on experience in GUI development using Tkinter.
• Learned how to integrate Python with MySQL databases.
• Implemented CRUD operations (Create, Read, Update, Delete).
• Enhanced skills in data validation and error handling.

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Contact management system

Contact Management System:

This is a Python-based Contact Management System developed using the Tkinter library for GUI and MySQL for backend data storage. The system allows efficient management of contacts by enabling users to add, delete, search, and display contacts. It also includes data validation to ensure the uniqueness of phone numbers and provides user feedback through message boxes.

Key Features:

• Add Contact: Insert new contact details like name, phone, email, and address.
• Delete Contact: Remove existing contacts from the database.
• Search Contact: Search contacts by name or phone number.
• Display Contacts: View all stored contacts in a structured format.
• Data Validation: Ensures the uniqueness of phone numbers.
• User Feedback: Provides success or error messages using message boxes.

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