Prostate Cancer Data Analysis

Overview

This project analyzes prostate cancer data using machine learning techniques to identify patterns and insights. The analysis is performed in a Jupyter Notebook using Python libraries such as Pandas, NumPy, Scikit-Learn, and Matplotlib.

Features

• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Feature selection and engineering

Installation

To run this project, follow these steps:

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the project directory:

   cd Prostate_Cancer_Data_Analysis

3. Install dependencies:

   pip install -r requirements.txt

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open and run Prostate_Cancer_Data_Analysis.ipynb

Requirements

Ensure you have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Usage

1. Load the dataset and explore key statistical insights.
2. Perform feature selection and preprocessing.
3. Visualize results for better interpretation.

Results

• Graphical insights into dataset trends.

Contributing

Feel free to fork this repository and contribute by submitting a pull request.

Author

Shraddha Saraf