This GitHub repository contains Python notebooks for four machine learning tasks:
The objective of this task is to create a machine learning model that predicts the genre of a movie based on its plot summary or other textual information. Techniques like TF-IDF and word embeddings are utilized along with classifiers such as Naive Bayes, Logistic Regression, or Support Vector Machines.
This task involves building a model to detect fraudulent credit card transactions using a dataset containing information about credit card transactions. Various algorithms, including Logistic Regression, Decision Trees, or Random Forests, are experimented with to classify transactions as fraudulent or legitimate.
The aim of this task is to develop a model to predict customer churn for a subscription-based service or business. Historical customer data, including features like usage behavior and customer demographics, is used. Algorithms like Logistic Regression, Random Forests, or Gradient Boosting are employed to predict churn.
This task involves building an AI model that can classify SMS messages as spam or legitimate. Techniques like TF-IDF or word embeddings are combined with classifiers such as Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages.
- Python 3.x
- Jupyter Notebooks
- Required libraries (scikit-learn, pandas, numpy, etc.) - Install using
pip install -r requirements.txt
-
Clone the repository:
git clone https://github.com/Santosh2611/NeuroNexus-Innovations.git cd NeuroNexus-Innovations/NeuroNexus Innovations - Machine Learning -
Install the required libraries:
pip install -r requirements.txt -
Open and run the respective notebooks for each task using Jupyter Notebook.
Feel free to explore, modify, and adapt the code for your own projects. If you encounter any issues or have suggestions for improvements, please create an issue or submit a pull request.
Happy coding! 🚀