CodeAlpha Machine Learning Internship

This repository contains the projects completed during the CodeAlpha Machine Learning Internship Program. The internship focused on applying Machine Learning and Deep Learning techniques to solve real-world problems involving classification, computer vision, speech processing, and healthcare analytics.

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Internship Tasks

Task 1: Credit Scoring Model

Objective:
Predict an individual's creditworthiness using historical financial information.

Techniques Used:

• Data Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Random Forest Classifier
• Model Evaluation

Dataset Features:

• Age
• Gender
• Housing Status
• Savings Account
• Checking Account
• Credit Amount
• Loan Duration
• Loan Purpose

Evaluation Metrics:

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

📂 Folder: Task1_Credit_Scoring_Model

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Task 2: Emotion Recognition from Speech

Objective:
Identify human emotions from speech audio using Deep Learning techniques.

Techniques Used:

• Audio Processing
• MFCC Feature Extraction
• Convolutional Neural Networks (CNN)
• Model Training & Validation

Recognized Emotions:

• Happy
• Sad
• Angry
• Fearful
• Neutral
• Disgust
• Surprised

Evaluation Metrics:

• Training Accuracy
• Validation Accuracy
• Training Loss
• Validation Loss

📂 Folder: Task2_Emotion_Recognition_From_Speech

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Task 3: Handwritten Character Recognition

Objective:
Recognize handwritten digits using Deep Learning and Computer Vision.

Techniques Used:

• Convolutional Neural Networks (CNN)
• Image Processing
• MNIST Dataset
• Classification

Features:

• Digit Recognition
• Confusion Matrix Analysis
• Training & Validation Monitoring
• Prediction Visualization

Dataset:

• MNIST Handwritten Digits Dataset

📂 Folder: Task3_Handwritten_Character_Recognition

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Task 4: Disease Prediction from Medical Data

Objective:
Predict breast cancer diagnosis using medical diagnostic features.

Techniques Used:

• Data Preprocessing
• Feature Scaling
• Logistic Regression
• Random Forest Classifier
• Medical Data Analysis

Dataset:

• Breast Cancer Wisconsin Dataset

Evaluation Metrics:

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

📂 Folder: Task4_Disease_Prediction_from_Medical_Data

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Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn
• TensorFlow
• Keras
• Google Colab

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Repository Structure

CodeAlpha_ML_Internship/
│
├── Task1_Credit_Scoring_Model/
│   ├── README.md
│   ├── source_code
│   ├── dataset
│   └── visualizations
│
├── Task2_Emotion_Recognition_From_Speech/
│   ├── README.md
│   ├── source_code
│   ├── dataset
│   └── visualizations
│
├── Task3_Handwritten_Character_Recognition/
│   ├── README.md
│   ├── source_code
│   ├── dataset
│   └── visualizations
│
├── Task4_Disease_Prediction_from_Medical_Data/
│   ├── README.md
│   ├── source_code
│   ├── dataset
│   └── visualizations
│
└── README.md

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Learning Outcomes

During this internship, the following concepts were explored:

• Machine Learning Classification
• Deep Learning with CNNs
• Audio Signal Processing
• Feature Engineering
• Medical Data Analysis
• Computer Vision
• Model Evaluation Techniques
• Data Visualization
• Exploratory Data Analysis

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Author

Radhika

Machine Learning Intern – CodeAlpha

GitHub: https://github.com/Radhika14911

LinkedIn:https://www.linkedin.com/in/radhika-mittal-04b4241ab/

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Acknowledgement

Special thanks to CodeAlpha for providing practical machine learning projects that helped strengthen skills in Machine Learning, Deep Learning, Data Analysis, and Artificial Intelligence.