https://github.com/shraddha-r0/pgp-ml-ai-portfolio
A series of six hands-on projects completed during my PGP ML and AI academic training with UT Austin and Great Learning
https://github.com/shraddha-r0/pgp-ml-ai-portfolio
artificial-neural-networks bagging-ensemble bivariate-analysis boosting-ensemble cross-validation data-pre-processing data-science decision-tree-classifier exploratory-data-analysis hyperparameter-tuning keras large-language-models machine-learning model-building-and-evaluation random-forest-classifier smote tensorflow transfer-learning-with-cnn univariate-analysis word-embeddings
Last synced: 3 months ago
JSON representation
A series of six hands-on projects completed during my PGP ML and AI academic training with UT Austin and Great Learning
- Host: GitHub
- URL: https://github.com/shraddha-r0/pgp-ml-ai-portfolio
- Owner: shraddha-r0
- Created: 2025-06-17T21:35:26.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-17T22:09:01.000Z (4 months ago)
- Last Synced: 2025-06-17T22:36:32.751Z (4 months ago)
- Topics: artificial-neural-networks, bagging-ensemble, bivariate-analysis, boosting-ensemble, cross-validation, data-pre-processing, data-science, decision-tree-classifier, exploratory-data-analysis, hyperparameter-tuning, keras, large-language-models, machine-learning, model-building-and-evaluation, random-forest-classifier, smote, tensorflow, transfer-learning-with-cnn, univariate-analysis, word-embeddings
- Language: Jupyter Notebook
- Homepage: https://www.mygreatlearning.com/eportfolio/shraddha-ramesh2
- Size: 9.73 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 Data Science Portfolio – PGP Projects
Welcome! This repository showcases a series of six hands-on projects developed as part of my academic training in data science and machine learning in the PGP AI and ML Program with UT Austin and Great Learning. Each folder contains a self-contained Jupyter notebook focused on a specific concept or application.
Below is an overview of the contents:
---
## 📊 Project 1 – FoodHub - Exploratory Data Analysis
*Course: Python Foundations*
Perform an **exploratory data analysis** and provide actionable insights for a food aggregator company to get a fair idea about the demand of different restaurants and cuisines, which will help them enhance their customer experience and improve the business
*Skills covered: python, numpy, pandas, seaborn, exploratory data analysis, business recommendations, bivariate analysis, univariate analysis*
---
## 🌲 Project 2 – Personal Loan Campaign - Decision Tree
*Course: Machine Learning*
To identify bank customers with a high likelihood of purchasing a loan, you need to analyze the provided data to understand key customer attributes influencing loan acquisition. With this analysis, build **a predictive model that captures patterns and customer characteristics**, which will help the bank effectively target potential loan buyers, improving marketing efforts and increasing conversion rates.
*Skills covered: exploratory data analysis, data pre-processing, model building, decision tree classifier, model performance evaluation and improvement, business recommendations*
---
## 📉 Project 3 – Churn Prediction - Ensemble Methods
*Course: Advanced Machine Learning*
Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it
*Skills covered: eda, random forest, bagging, boosting, smote, cross validation, data preprocessing, hyperparameter tuning*
---
## 🧠 Project 4 – Churn Prediction - Neural Networks
*Course: Introduction to Neural Networks*
Analyze the customer data, build a **neural network** to help the operations team identify the customers that are more likely to churn, and provide recommendations on how to retain such customers
*Skills covered: eda, tensorflow, keras, artificial neural networks, regularization*
---
## 👁️ Project 5 – Seedling Classification - Computer Vision
*Course: Introduction to Computer Vision*
Build a robust image classifier using **convolutional neural networks** to efficiently classify different plant seedlings and weeds to improve crop yields and minimize human involvement
*Skills covered: image processing, keras, tensorflow, convulational neural networks, transfer learning*
---
## 💬 Project 6 – Stock Market News Sentiment Analysis and Summarization - Natural Language Processing
*Course: Introduction to Natural Language Processing*
Develop an an **AI-driven sentiment analysis** system that will automatically process and analyze news articles to gauge market sentiment, and summarize the news at a weekly level to enhance the accuracy of their stock price predictions and optimize investment strategies.
*Skills covered: large language models, text processing, transformers, prompt engineering, data manipulation, word embeddings, word2vec, glove*
---
### 📌 Notes:
- All projects follow clean coding practices, include inline explanations, and use standard libraries (`scikit-learn`, `keras`, `seaborn`, etc.).
- Each notebook is designed to be understandable and reproducible.
---
Thanks for stopping by! ✨