CodSoft Machine Learning Internship Projects

-This repository contains three machine learning projects completed as part of the CodSoft Machine Learning Internship (December Batch).

-The projects focus on classical machine learning fundamentals, correct problem formulation, and appropriate evaluation metrics using Python and scikit-learn.

-The objective is learning and clarity of implementation rather than production deployment.

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Repository Overview

-Three independent machine learning tasks -Text and binary classification problems -Emphasis on data preprocessing and evaluation -Handling of imbalanced datasets -Clean, reproducible project structure

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Tasks Included

-Task 1 — Movie Genre Classification -Text classification using movie descriptions -Feature extraction with TF-IDF -Multi-class model evaluation -Folder: Task 1 - Movie Genre Classification/

-Task 2 — Credit Card Fraud Detection -Binary classification on highly imbalanced data -Focus on precision, recall, and false negatives -Evaluation beyond accuracy -Folder: Task_2_Credit_card_fraud_detection/

-Task 3 — SMS Spam Detection -Binary text classification -NLP preprocessing and TF-IDF -Model comparison and selection -Folder: Task_3_SMS_Spam_Detection/

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Models Used

-Logistic Regression -Naive Baye -Decision Trees -Support Vector Machine -Random Forest (where applicable)

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Evaluation Metrics

-Confusion Matrix -Precision -Recall -F1-Score Metrics are selected based on problem characteristics, especially for imbalanced datasets.

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Repository Structure

CODSOFT/
├── Task 1 - Movie Genre Classification/
├── Task_2_Credit_card_fraud_detection/
├── Task_3_SMS_Spam_Detection/
└── README.md

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Tools and Libraries

-Python -NumPy -Pandas -scikit-learn -Matplotlib

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Internship Outcome

-This internship provided hands-on experience in: -End-to-end machine learning workflows -NLP-based classification tasks -Model evaluation and comparison -Writing clean, well-documented machine learning code