CursorCustomModels

A collection of Machine Learning and Data Science projects implemented in Python using Jupyter Notebooks. This repository covers various real-world prediction and classification problems across healthcare, finance, aerospace, and insurance domains.

---

📁 Repository Structure

CursorCustomModels/
├── Credit Card Fraud Detction.ipynb      # Credit card fraud detection using ML
├── Heart_disease.ipynb                   # Heart disease prediction model
├── heart_disease_data.csv                # Dataset for heart disease prediction
├── Himanshu.ipynb                        # Stock market data analysis with yfinance
├── insurance.csv                         # Dataset for medical insurance cost prediction
├── LAB-1-_1.IPY                          # Lab exercise notebook
├── LAB-FR_1.IPY                          # Lab exercise notebook
├── Machine Learning & Data Science Project.ipynb  # End-to-end ML/DS project
├── Medical Insurance cost Prediction.ipynb        # Insurance cost prediction using regression
├── ML Prediction.ipynb                   # SpaceX Falcon 9 landing prediction
├── svm.ipynb                             # Support Vector Machine (SVM) implementation
├── Untitled.ipynb                        # Draft/experimental notebook
└── file.ipynb                            # General purpose notebook

---

🚀 Projects Overview

1. Credit Card Fraud Detection

File: Credit Card Fraud Detction.ipynb

A machine learning model to detect fraudulent credit card transactions. The project involves:

• Data preprocessing and handling imbalanced datasets
• Exploratory Data Analysis (EDA)
• Classification using various ML algorithms
• Model evaluation with precision, recall, and F1-score

---

2. Heart Disease Prediction

File: Heart_disease.ipynb Dataset: heart_disease_data.csv

Predicts the likelihood of heart disease based on patient health metrics. Covers:

• Data cleaning and feature engineering
• Statistical analysis of health indicators
• Classification models (Logistic Regression, Random Forest, etc.)
• Performance comparison across algorithms

---

3. Medical Insurance Cost Prediction

File: Medical Insurance cost Prediction.ipynb Dataset: insurance.csv

A regression-based project to predict medical insurance costs based on:

• Age, BMI, smoking status, region, and number of children
• Data visualization and correlation analysis
• Linear Regression and other regression techniques
• Model accuracy evaluation

---

4. SpaceX Falcon 9 First Stage Landing Prediction

File: ML Prediction.ipynb

An end-to-end machine learning pipeline to predict whether the SpaceX Falcon 9 first stage will successfully land. This project includes:

• Data collection and API integration
• Feature engineering from launch data
• Classification models to predict landing success
• Visualization of launch outcomes

---

5. Support Vector Machine (SVM)

File: svm.ipynb

A dedicated notebook exploring Support Vector Machine concepts:

• SVM theory and mathematical intuition
• Kernel tricks and hyperparameter tuning
• Classification on sample datasets
• Visualization of decision boundaries

---

6. Stock Market Data Analysis

File: Himanshu.ipynb

Financial data analysis using the yfinance library:

• Fetching historical stock market data
• Time series analysis and trend visualization
• Technical indicators and statistical summaries

---

7. Machine Learning & Data Science Project

File: Machine Learning & Data Science Project.ipynb

A comprehensive end-to-end project covering the full ML lifecycle:

• Data ingestion and preprocessing
• Exploratory Data Analysis with visualizations
• Model training, tuning, and evaluation
• Result interpretation and insights

---

🛠️ Technologies & Libraries Used

• Python 3.x
• Jupyter Notebooks
• Pandas – Data manipulation and analysis
• NumPy – Numerical computing
• Scikit-learn – Machine learning algorithms and model evaluation
• Matplotlib & Seaborn – Data visualization
• yfinance – Financial data retrieval
• Plotly – Interactive visualizations (where applicable)

---

📊 Datasets

Dataset	File	Description
Heart Disease	heart_disease_data.csv	Patient health records for heart disease prediction
Medical Insurance	insurance.csv	Demographic and health data for insurance cost prediction

---

🎯 Getting Started

Prerequisites

Ensure you have Python installed along with the following packages:

pip install pandas numpy scikit-learn matplotlib seaborn yfinance plotly jupyter

Running the Notebooks

1. Clone the repository:

   git clone https://github.com/Himanshuvishwa007/CursorCustomModels.git
   cd CursorCustomModels

2. Launch Jupyter Notebook:

   jupyter notebook

3. Open any .ipynb file and run the cells sequentially.

---

📈 Key Learning Outcomes

• Hands-on experience with real-world datasets
• Understanding of classification vs. regression problems
• Feature engineering and data preprocessing techniques
• Model selection, training, and hyperparameter tuning
• Evaluation metrics: Accuracy, Precision, Recall, F1-Score, RMSE, R²
• Data visualization for insights and storytelling

---

🤝 Contributing

Contributions are welcome! If you'd like to improve any notebook, add new projects, or fix issues:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

---

📝 License

This repository is for educational and learning purposes. Feel free to use the code and notebooks for your own learning.

---

👤 Author

Himanshu Vishwakarma

• GitHub: @Himanshuvishwa007

---

⭐ If you find this repository helpful, consider giving it a star!