https://github.com/himanshumahajan138/prediction-using-decision-tree-algorithm
https://github.com/himanshumahajan138/prediction-using-decision-tree-algorithm
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/himanshumahajan138/prediction-using-decision-tree-algorithm
- Owner: himanshumahajan138
- License: mit
- Created: 2023-09-21T07:29:49.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-21T07:44:17.000Z (about 2 years ago)
- Last Synced: 2025-03-22T03:42:40.481Z (7 months ago)
- Language: Jupyter Notebook
- Size: 488 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Science Internship Task 2 - [The Sparks Foundation](https://thesparksfoundationsingapore.org/)
# Prediction-Using-Decision-Tree-Algorithm
## Introduction
This repository contains the code and explanation for Task 2 of my Data Science internship at [The Sparks Foundation](https://thesparksfoundationsingapore.org/). In this task, I have analyzed the Iris dataset, a classic dataset often used in machine learning for classification tasks.
## About [The Sparks Foundation](https://thesparksfoundationsingapore.org/)
The Sparks Foundation is a non-profit organization committed to providing opportunities for students and professionals to develop skills in various fields, including data science and machine learning. They offer a range of tasks and projects to help individuals gain practical experience and knowledge in these domains.
## Project Overview
The project is a Python script that performs a comprehensive analysis of the Iris dataset. Here's an overview of what the code does:
- Imports necessary libraries for data analysis, visualization, and modeling.
- Loads the Iris dataset from a CSV file into a DataFrame and preprocesses the data.
- Explores the data, checks for null values, and handles outliers.
- Visualizes the relationships between variables.
- Splits the data into training and testing sets.
- Applies feature scaling to ensure consistent scales.
- Builds a Decision Tree Classifier model and evaluates its performance.
- Visualizes the Decision Tree for insights.
## Usage
To use this code and analyze the Iris dataset, follow these steps:
1. Clone this repository to your local machine: ```git clone https://github.com/himanshumahajan138/Prediction-Using-Decision-Tree-Algorithm```
2. Install the required libraries if you haven't already. You can use pip for this: ```pip install pandas numpy matplotlib seaborn scikit-learn```
3. Download the Iris dataset (Iris.csv) and place it in the same directory as the script.
4. Run the script: ```python iris_analysis.py```
5. The script will perform data analysis, build a model, and display the results and visualizations.
## Acknowledgments
- [The Sparks Foundation](https://thesparksfoundationsingapore.org/) for providing this internship opportunity and the task.
- The Iris dataset, a classic dataset for machine learning, available through various sources.
Feel free to explore and modify the code to suit your own data analysis projects. Happy coding!
## Author
**Himanshu**
*Data Science Intern*
*[The Sparks Foundation](https://thesparksfoundationsingapore.org/)*
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.