🤖 CodSoft Machine Learning Projects

This repository contains the Machine Learning projects completed as part of the CodSoft Machine Learning Internship.

📌 Projects Included

1. Fraud Detection

• Detect fraudulent financial transactions using machine learning.
• Includes data preprocessing, feature engineering, model training, and evaluation.

Tech Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

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2. Iris Flower Classification

A classic machine learning classification project that predicts the species of Iris flowers based on flower measurements.

Dataset

• Iris Dataset

Algorithms

• Logistic Regression
• Decision Tree (if used)
• Random Forest (if used)

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3. Movie Rating Prediction

Predict movie ratings using machine learning techniques based on available movie features.

Tech Used

• Regression Models
• Data Cleaning
• Feature Engineering

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4. Titanic Survival Prediction

Predict whether a passenger survived the Titanic disaster using passenger information.

Dataset

• Titanic Dataset

Features

• Data Cleaning
• Feature Encoding
• Model Training
• Accuracy Evaluation

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🛠 Technologies Used

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

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📂 Repository Structure

├── FRAUD_DETECTION.ipynb
├── Iris_.ipynb
├── Movie_Rating.ipynb
├── Titanic_Survival_prediction_ipynb.ipynb
├── README.md

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📊 Libraries Used

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

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🎯 Internship

These projects were completed during the CodSoft Machine Learning Internship to gain practical experience in:

• Data Preprocessing
• Exploratory Data Analysis
• Machine Learning
• Model Evaluation
• Python for Data Science

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👨‍💻 Author

Athulya Suresh

LinkedIn: https://www.linkedin.com/in/athulya-m-suresh/

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