sumit-nimbale/CODSOFT

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.

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

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/

Models Used

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

Evaluation Metrics

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

Repository Structure

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

Tools and Libraries

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

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