https://github.com/ashish4kmax/data_science-mini-project

https://github.com/ashish4kmax/data_science-mini-project

Last synced: 29 days ago
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• Host: GitHub
• URL: https://github.com/ashish4kmax/data_science-mini-project
• Owner: ashish4kmax
• Created: 2024-02-07T13:02:55.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-02-07T13:07:25.000Z (11 months ago)
• Last Synced: 2024-02-07T14:25:45.241Z (11 months ago)
• Language: Python
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data_Science-Mini-Project

* The provided code is an implementation of the decision tree algorithm for a classification task using the ID3 (Iterative Dichotomiser 3) algorithm. Here's a breakdown of the prerequisites and setup for this code:

* Python Environment: You need a Python environment set up on your system to run this code. Ensure you have Python installed, preferably version 3.x.

* Libraries: The code relies on the following libraries:

* pandas: Used for data manipulation and analysis. It provides data structures like DataFrame, which is utilized to store and manipulate the dataset.

* math: Imported for mathematical calculations, specifically for computing logarithms.

* Dataset: The code operates on a sample weather dataset provided in the 'data' dictionary. This dataset contains attributes such as Outlook, Temperature, Humidity, Windy, and Play (the target attribute). You can see the dataset initialization at the beginning of the code.

* DataFrame Creation: The code creates a pandas DataFrame named 'df' using the sample weather dataset. This DataFrame organizes the data in tabular form, which is convenient for analysis and manipulation.

* Entropy Calculation: The entropy() function calculates the entropy of a target column. Entropy is a measure of impurity in a dataset.

* Information Gain Calculation: The information_gain() function computes the information gain of a dataset for a given attribute. Information gain measures the effectiveness of an attribute in classifying the data.

* Printing Results: The code prints the entropy of the 'Play' attribute and the information gain for each attribute. It then identifies the best attribute for splitting the dataset based on the maximum information gain.

* To run this code, you simply need to execute it in a Python environment after ensuring that the necessary libraries (pandas) are installed. You can either use an integrated development environment (IDE) like PyCharm, Jupyter Notebook, or run it from the command line. Make sure the sample dataset provided is accurate and representative of your problem domain, or you can replace it with your own dataset.

   

# Below are the commands to install the libraries before starting:-

* pip install pandas

* pip install math