Overview This repository contains four machine learning capstone projects focusing on predictive modeling in different domains: airline pricing, heart disease detection, insurance cost estimation, and house price prediction. Each project involves data preprocessing, model building, evaluation, and interpretation to achieve high accuracy and reliability.
Projects
-
Flight Price Prediction (Accuracy: 85%) Predicts airline ticket prices based on factors such as airline, source, destination, departure time, duration, and number of stops. Useful for travelers and companies for better planning and pricing.
-
Heart Disease Prediction (Accuracy: 80%) Predicts the presence of heart disease in individuals based on various medical attributes such as blood pressure, cholesterol, and maximum heart rate achieved. Assists healthcare professionals in early detection and treatment planning.
-
Insurance Cost Prediction (Accuracy: 90%) Predicts the medical insurance charges for individuals based on their age, gender, BMI, number of children, smoking status, and region. Supports both insurers and customers in understanding and planning medical costs.
-
House Price Prediction (Accuracy: 90%) Predicts housing prices based on features like area, number of bedrooms and bathrooms, location, and furnishing status. Helps buyers, sellers, and real estate investors make informed decisions.
Common Challenges Faced Presence of null values and missing data
Detection and treatment of outliers
Handling of categorical features (e.g., airline names, regions, chest pain types)
Data imbalance in classification problems (especially in Heart Disease Prediction)
Feature scaling for certain algorithms
Ensuring the model generalizes well and does not overfit
Tools and Technologies Used Programming Language: Python
Libraries:
Pandas
NumPy
Matplotlib
Seaborn
Scikit-learn
Environment: Jupyter Notebook
Algorithms Used Regression Models:
Linear Regression
Random Forest Regressor
XGBoost Regressor
Classification Models:
Logistic Regression
Random Forest Classifier
Folder Structure Copy Edit ML-Capstone-Prediction-Projects/ │ ├── Flight_Price_Prediction/ │ ├── flight_price_prediction.ipynb │ └── README.md │ ├── Heart_Disease_Prediction/ │ ├── heart_disease_prediction.ipynb │ └── README.md │ ├── Insurance_Cost_Prediction/ │ ├── insurance_cost_prediction.ipynb │ └── README.md │ ├── House_Price_Prediction/ │ ├── house_price_prediction.ipynb │ └── README.md │ └── README.md (This common file) How to Run Clone the repository.
Install required libraries:
nginx Copy Edit pip install pandas numpy matplotlib seaborn scikit-learn Open each project's Jupyter Notebook (.ipynb file).
Execute the cells step-by-step to view the full workflow: from data analysis to model building and prediction.
Future Enhancements Hyperparameter tuning using GridSearchCV or RandomizedSearchCV
Model deployment using Flask or Streamlit
Adding dashboards and visualizations for better interpretation
Deployment on cloud platforms like AWS/GCP
Thank you for checking out my projects! Feel free to explore each folder for detailed notebooks and explanations.