CodeAlpha Machine Learning Internship

This repository contains the projects completed as part of the CodeAlpha Machine Learning Internship. Each task focuses on applying machine learning techniques to solve real-world problems using Python and popular ML libraries.

Tasks Completed

Task 1: Credit Scoring Model

Built a credit scoring model to predict an individual's creditworthiness based on historical financial data. Feature engineering and classification techniques were applied to improve prediction performance.

Task 4: Disease Prediction from Medical Data

Developed classification models to predict diseases using structured medical datasets. Multiple machine learning algorithms were implemented and compared to identify the best-performing model.

Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost
• Jupyter Notebook

Machine Learning Algorithms

• Logistic Regression
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• XGBoost

Workflow

• Data Collection
• Data Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Feature Scaling
• Model Training
• Model Evaluation
• Performance Comparison
• Prediction

Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC Score
• Confusion Matrix

Repository Structure

CodeAlpha-Machine-Learning/
│── task1/
│── task1/
│── README.md

Learning Outcomes

• Data preprocessing and feature engineering
• Exploratory data analysis and visualization
• Classification using multiple machine learning algorithms
• Model evaluation and comparison
• Building predictive machine learning solutions for real-world datasets

Acknowledgement

This repository contains projects completed during the CodeAlpha Machine Learning Internship as part of the assigned tasks.

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Author: Suhanee Anand