codebank 0.2.3
tool for cheating exam
Requires Python: >=3.10


## **codebank**

### A Python Package for Easy Access to Predefined Code Snippets for Exams

This package provides easy access to predefined Python code snippets that can be directly used in exams or coding assessments. By calling simple functions, users can retrieve code examples on various topics, copy the output from the console, and paste it into their compiler or editor for execution.

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### **Features**
- Predefined code snippets for common Python problems.
- Easy-to-use function calls.
- Useful for exam preparation and quick reference.
  
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### **Installation**

To install the package, use pip:

```bash
pip install codebank
```

---

### **Usage**

After installation, you can import the package and start retrieving predefined code snippets by calling simple functions.

#### **Example 1: Retrieve a Function to Find Prime Numbers**

```python
import codebank

# Call the required function to retrieve the code
code_snippet = codebank.one()   

# The code will be printed in the console, and you can copy it
print(code_snippet)
```

Output:
```python
import numpy as np
import pandas as pd
df=pd.read_csv('Iris1.csv')
df.head()
from sklearn import tree
from sklearn.model_selection import train_test_split
X=df[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]
Y=df['Species']
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2)
model=tree.DecisionTreeClassifier()
model.fit(X_train,Y_train)
print("score",model.score(X_test,Y_test))
tree.plot_tree(model)

```

### **Available Functions**

- `one()`: Decision Tree Classifier
- `two()`: Spam Detection using Support Vector Machine
- `three()`: Face Detection using Cascading
- `four()`: Face Recognisation using ANN
- `five()`: K Nearest Neighbour
- `fivea()`: Adult Salary Prediction using Random Forest
- `six()`: Multi Layer Perceptron for Character Recognisation
- `seven()`: Sentiment Analysis using Random Forest 
- `eight()`: Bayesian Network for Heart Disease
- `nine()`: Fraud Detection using ANN

- More functions can be added to cover additional topics such as searching, recursion, data structures, etc.

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### **How to Use**

1. **Copy the Code**: After calling a function, copy the printed code snippet from the console.
2. **Paste and Run**: Paste it into your Python editor or compiler.
3. **Modify (Optional)**: If required, you can modify the function parameters or extend the code as per your needs.

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### **Contributing**

Contributions are welcome! If you would like to add more code snippets or suggest improvements, feel free to fork the repository and submit a pull request.

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### **License**

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

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### **Feedback**

For issues, bugs, or feature requests, feel free to open an issue on the [GitHub repository](https://github.com/yourusername/your_package_name).

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This README provides a simple and clear guide for users, focusing on ease of use and practical functionality for exam preparation. It walks through installation, usage examples, available functions, and contribution guidelines.