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.
- Logistic Regression 📈
- Gradient Boosting 🚀
- Random Forests 🌲
- Clone the repository:
git clone https://github.com/yourusername/NeuroNexus-Projects.git - Navigate to the project folder:
cd NeuroNexus-Projects/Churn_Prediction - Install the required dependencies:
pip install -r requirements.txt - Run the notebook:
jupyter notebook churn_prediction.ipynb
Include any key findings, metrics, or visualizations from your analysis.
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.
- TF-IDF Vectors 📊
- Naive Bayes 🧠
- Clone the repository:
git clone https://github.com/yourusername/NeuroNexus-Projects.git - Navigate to the project folder:
cd NeuroNexus-Projects/Spam_Ham_Identification - Install the required dependencies:
pip install -r requirements.txt - Run the notebook:
jupyter notebook spam_ham_identification.ipynb
Include any key findings, metrics, or visualizations from your analysis.
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.
This project is licensed under the MIT License.