https://github.com/anidipta/afame-technologies-ml

https://github.com/anidipta/afame-technologies-ml

Last synced: 22 days ago
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• Host: GitHub
• URL: https://github.com/anidipta/afame-technologies-ml
• Owner: Anidipta
• License: apache-2.0
• Created: 2024-04-16T17:38:57.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-08-19T16:42:18.000Z (5 months ago)
• Last Synced: 2024-08-19T20:57:34.359Z (5 months ago)
• Language: Jupyter Notebook
• Size: 3.33 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# 🚀 Afame Technologies ML

## 🌟 Projects Completed - **2**

### Task 2 -> **📉 CUSTOMER CHURN PREDICTION**

**🗂 Dataset:**  

This dataset contains information about U.S. bank customers, providing insights into whether a particular customer is likely to leave the bank or not.

**🎯 Objective:**  

The goal is to develop a predictive model for customer churn in a subscription-based service or business. Using historical customer data, including usage behavior and demographics, we will apply machine learning algorithms like Logistic Regression, Random Forests, or Gradient Boosting to predict customer churn.

### Task 4 -> **📲 SPAM SMS DETECTION**

**🗂 Dataset:**  

The SMS Spam Collection is a dataset of 5,574 SMS tagged messages collected for spam research. Each message is labeled as either ham (legitimate) or spam.

**🎯 Objective:**  

The aim is to build an AI model capable of classifying SMS messages as either spam or legitimate. Techniques like TF-IDF and word embeddings will be combined with classifiers such as Naive Bayes, Logistic Regression, or Support Vector Machines to accurately identify spam messages.