This repository contains projects completed during my AI/ML Engineering Internship. These projects demonstrate the application of machine learning, artificial intelligence, data analysis, prompt engineering, and chatbot development techniques to solve real-world problems.
Objective: Classify iris flowers into different species based on flower measurements. Dataset: Iris Dataset Models Applied: Logistic Regression, Decision Tree, Random Forest Key Result: Achieved high classification accuracy and identified petal measurements as the most important features.
Objective: Predict the likelihood of heart disease using patient health data. Dataset: Heart Disease Dataset Models Applied: Logistic Regression, Decision Tree, Random Forest Key Result: Successfully identified potential heart disease risk factors and generated accurate predictions.
Objective: Forecast short-term stock prices using historical market data. Dataset: Historical Stock Market Dataset Models Applied: Linear Regression, Random Forest Regressor Key Result: Generated short-term stock price predictions and analyzed market trends.
Objective: Develop a chatbot capable of answering general health-related questions. Dataset: Prompt Engineering Based Project Models Applied: Large Language Model with Prompt Engineering Techniques Key Result: Delivered accurate and context-aware responses to common health queries.
Objective: Build a fine-tuned chatbot that provides supportive and empathetic mental health conversations. Dataset: Mental Health Conversation Dataset Models Applied: Fine-Tuned Language Model Key Result: Improved response quality and contextual understanding through fine-tuning.
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Natural Language Processing (NLP)
- Machine Learning
- Prompt Engineering
- Fine-Tuning Techniques
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization
- Classification Models
- Regression Models
- Model Evaluation
- Chatbot Development
- Prompt Engineering
- Fine-Tuning AI Models
Each project folder contains:
- Source Code
- Dataset Information
- Model Training Process
- Evaluation Results
- Visualizations
- Project Documentation
- Implement advanced deep learning models.
- Improve model performance through hyperparameter tuning.
- Deploy projects as web applications.
- Integrate real-time data sources where applicable.
- Enhance chatbot capabilities with larger datasets and improved context handling.