Author: D A R S H A N S
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Welcome to my internship showcase as part of the CodSoft Internship September 2023 with CODSOFT.
In this repository, I present three of my significant tasks I completed during my internship:
Create a machine learning model that can predict the genre of a movie based on its plot summary or other textual information. You can use techniques like TF-IDF or word embeddings with classifiers such as Naive Bayes, Logistic Regression, or Support Vector Machines.
For this task, I implemented an ML model using TF-IDF and Naive Bayes classifier to successfully predict movie genres based on plot summaries, achieving accurate genre classification.
Build a model to detect fraudulent credit card transactions. Use a dataset containing information about credit card transactions, and experiment with algorithms like Logistic Regression, Decision Trees, or Random Forests to classify transactions as fraudulent or legitimate.
In this task, I developed a fraud detection model utilizing algorithms such as Logistic Regression, Decision Trees, and Random Forests on a credit card transaction dataset. The code and output files are available in the respective project directory.
Build an AI model that can classify SMS messages as spam or legitimate. Use techniques like TF-IDF or word embeddings with classifiers like Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages.
For this project, I successfully implemented an AI model utilizing TF-IDF and classifiers like Naive Bayes, Logistic Regression, and Support Vector Machines to distinguish between spam and legitimate SMS messages, significantly improving message filtering and user experience.
I'm excited to share the outcomes of all my tasks, and I encourage you to explore the respective project work in detail using the corresponding folders above containing both the source and main.py python executable.
Feel free to reach out if you have any questions or feedback. Sincerely Thank You for reviewing my internship work!
- URL for the origin repository is as below