mayank-s7 / codsoft

CodSoft

Welcome to the CodSoft Machine Learning Internship Project repository! This repository showcases the culmination of a challenging yet enriching internship experience aimed at mastering various aspects of machine learning.

Overview

This project was part of a four-week virtual internship at CodSoft, and it focused on three key tasks:

1. Prediction of Customer Churn The purpose was to analyze data on customer behavior and build predictive models to detect possible churners. We wanted to build ways to keep important clients by leveraging machine learning algorithms and predictive analytics.

2. Spam SMS Filtering The challenge entailed creating a classifier to differentiate between spam and non-spam text messages. We hoped to design an efficient method for filtering undesirable messages using natural language processing techniques and classification algorithms.

3. Detection of Credit Card Fraud The goal here was to create a comprehensive fraud detection system. The goal was to properly identify fraudulent transactions and minimise financial risks by using anomaly detection methods and supervised learning algorithms.

Internship Experience

During this internship, I delved deep into the world of machine learning, honing my skills in data preprocessing, feature engineering, model selection, and evaluation. The challenges posed by these tasks enabled me to apply various machine learning algorithms, such as Decision Trees, Random Forests, Support Vector Machines, and Neural Networks.

Through this internship, I acquired hands-on experience in:

Data preprocessing and cleaning techniques Feature selection and engineering methods Model development, tuning, and evaluation Interpretation of machine learning results Deploying machine learning models Growth and Future Prospects This internship has enhanced my proficiency in machine learning, allowing me to witness tangible growth in my skill set. The exposure to real-world problems and the application of cutting-edge techniques has equipped me to take on more challenging projects in the field.