/Neuro-Nexux-Innovations

🚀 Dive into predicting customer churn with gender, age, and more using Logistic Regression, Gradient Boosting, and Random Forests. 📬 Uncover the secrets of message classification with TF-IDF Vectors and Naive Bayes for spam or ham identification. Explore the magic of machine learning at NeuroNexus!

Primary LanguagePython

🧠 NeuroNexus Machine Learning Internship Projects 🤖

Project 1: Customer Churn Prediction 🔄

Overview

This project aims to predict customer churn using recorded, observed, and historical data. The dataset includes features such as gender, age, balance, salary, credit card holding, etc. The goal is to develop a robust model to identify potential churners.

Models Used

  1. Logistic Regression 📈
  2. Gradient Boosting 🚀
  3. Random Forests 🌲

How to Use

  1. Clone the repository: git clone https://github.com/yourusername/NeuroNexus-Projects.git
  2. Navigate to the project folder: cd NeuroNexus-Projects/Churn_Prediction
  3. Install the required dependencies: pip install -r requirements.txt
  4. Run the notebook: jupyter notebook churn_prediction.ipynb

Results

Include any key findings, metrics, or visualizations from your analysis.

Project 2: Spam or Ham Message Identification 📬

Overview

This project involves identifying messages as spam or ham by analyzing the excessive use of suspected words, caps-lock, exclamation, etc. The model utilizes TF-IDF vectors and Naive Bayes algorithm for effective classification.

Models Used

  1. TF-IDF Vectors 📊
  2. Naive Bayes 🧠

How to Use

  1. Clone the repository: git clone https://github.com/yourusername/NeuroNexus-Projects.git
  2. Navigate to the project folder: cd NeuroNexus-Projects/Spam_Ham_Identification
  3. Install the required dependencies: pip install -r requirements.txt
  4. Run the notebook: jupyter notebook spam_ham_identification.ipynb

Results

Include any key findings, metrics, or visualizations from your analysis.

Conclusion 🌟

Feel free to add a concluding section summarizing your overall experience, challenges faced, and lessons learned during the internship. Additionally, mention any future improvements or extensions for these projects.

License

This project is licensed under the MIT License.