LP-3 Laboratory Practice - How to Run

This guide provides instructions for running all projects in the LP-3 directory.

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📁 Directory Structure

LP-3/
├── BT/                 # Blockchain Technology (Solidity Smart Contracts)
├── DAA Python/         # Design and Analysis of Algorithms (Python)
├── ML/                 # Machine Learning (Jupyter Notebooks)
└── Dataset/            # Datasets for ML projects

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🔗 Blockchain Technology (BT)

Prerequisites

• Install Remix IDE (Online): https://remix.ethereum.org/
• Or install Ganache + Truffle for local development

How to Run Solidity Files

Method 1: Using Remix IDE (Recommended for Beginners)

1. Open https://remix.ethereum.org/ in your browser
2. Create a new file in Remix with .sol extension
3. Copy the code from any of the following files:
   • 3_Bank_account.sol - Bank account smart contract
   • 3.1_Product_Inventory.sol - Product inventory management
   • 4_Student.sol - Student record management
   • 4.1_Employee.sol - Employee record management
4. Paste the code into Remix
5. Select appropriate Solidity compiler version (check pragma statement in file)
6. Click "Compile" button
7. Go to "Deploy & Run Transactions" tab
8. Select "JavaScript VM" environment
9. Click "Deploy"
10. Interact with deployed contract functions

Method 2: Using Ganache + Truffle (Local Development)

npm install -g truffle
npm install -g ganache-cli

REM Start Ganache
ganache-cli

REM In new terminal, compile contracts
truffle compile

REM Deploy contracts
truffle migrate

REM Interact with contracts
truffle console

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🐍 Design and Analysis of Algorithms (DAA Python)

Prerequisites

• Python 3.8 or higher
• Install required packages:

pip install numpy matplotlib

How to Run Python Files

1. Fibonacci Numbers

python "DAA Python/1_fibonacci_numbers.py"

Description: Implements recursive and iterative Fibonacci calculation with time complexity analysis.

2. Huffman Encoding

python "DAA Python/2_huffman_encoding.py"

Description: Implements Huffman encoding algorithm for data compression.

3. Fractional Knapsack

python "DAA Python/3_fractional_knapsack.py"

Description: Solves fractional knapsack problem using greedy approach.

4. 0-1 Knapsack

python "DAA Python/4_0_1_knapsack.py"

Description: Solves 0-1 knapsack problem using dynamic programming.

5. N-Queens Problem

python "DAA Python/5_n_queens.py"

Description: Solves N-Queens problem using backtracking algorithm.

6. Quick Sort

python "DAA Python/6_QuickSort.py"

Description: Implements Quick Sort with deterministic and randomized pivot selection.

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🤖 Machine Learning (ML)

Prerequisites

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab
• Install required packages:

pip install -r requirements.txt

Or install individually:

pip install pandas numpy matplotlib seaborn scikit-learn tensorflow keras jupyter opendatasets

How to Run Jupyter Notebooks

Start Jupyter Notebook

cd LP-3\ML
jupyter notebook

Or Start JupyterLab

cd LP-3\ML
jupyter lab

Available Notebooks

1. Uber Price Detection

jupyter notebook "1. Uber Price Detection.ipynb"

Description: Predicts uber ride prices using linear regression.
Dataset: Dataset/1-uber.csv

2. Email Spam Classification

jupyter notebook "2. Email Spam Classification.ipynb"

Description: Classifies emails as spam or ham using machine learning.
Dataset: Dataset/2-email.csv

3. Neural Network

jupyter notebook "3. Neural_Network.ipynb"

Description: Implements artificial neural network for classification.
Dataset: Dataset/3-Churn_Modelling.csv

4. Gradient Descent

jupyter notebook "4. GradientDescent.ipynb"

Description: Demonstrates gradient descent optimization algorithm.
Dataset: Built-in dataset

5. KNN on Diabetes

jupyter notebook "5. KNN_on_Diabetes.ipynb"

Description: Applies K-Nearest Neighbors algorithm on diabetes dataset.
Dataset: Dataset/4-diabetes.csv

6. KMeans on Sales

jupyter notebook "6. KMeans_on_sales.ipynb"

Description: Performs customer segmentation using K-Means clustering.
Dataset: Dataset/5-sales_data_sample.csv

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📊 Alternative: Run Notebooks in VS Code

Prerequisites

1. Install Python extension in VS Code
2. Install Jupyter extension in VS Code

Steps

1. Open VS Code
2. Open the LP-3 folder
3. Navigate to ML/ directory
4. Click on any .ipynb file
5. Select Python kernel (top-right corner)
6. Run cells using Shift+Enter or click "Run All"

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🔧 Troubleshooting

Python Issues

• Module not found: Run pip install <module_name>
• Python not recognized: Add Python to system PATH
• Jupyter not starting: Try python -m jupyter notebook

Solidity Issues

• Compiler error: Check pragma version and use matching compiler
• Deployment fails: Ensure sufficient gas limit
• Function not visible: Check function visibility (public/private)

Dataset Issues

• File not found: Ensure you're running from correct directory
• Dataset missing: Check Dataset/ folder for required CSV files
• Encoding error: Try adding encoding='unicode_escape' or encoding='utf-8'

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📝 Notes

• All Python files can be run directly from command line
• Jupyter notebooks should be run in browser or VS Code for interactive experience
• Solidity contracts require Ethereum development environment
• Datasets are included in Dataset/ folder for ML projects
• Some notebooks may download datasets from Kaggle (requires opendatasets package)

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🎯 Quick Start Commands

REM Install all Python dependencies
pip install -r requirements.txt

REM Start Jupyter for ML projects
cd LP-3\ML
jupyter notebook

REM Run any DAA Python file
cd LP-3
python "DAA Python/1_fibonacci_numbers.py"

REM Open Remix IDE for BT projects
start https://remix.ethereum.org/

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📚 Additional Resources

• Remix IDE Documentation: https://remix-ide.readthedocs.io/
• Solidity Documentation: https://docs.soliditylang.org/
• Python Documentation: https://docs.python.org/3/
• Scikit-learn Documentation: https://scikit-learn.org/
• TensorFlow Documentation: https://www.tensorflow.org/

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#sahil chatgpt theory - https://chatgpt.com/share/691550ba-fba8-8011-9041-964b801fec4c #omkar chatgpt theory - https://chatgpt.com/share/691344b0-9eb8-8003-ab2e-73b68fd322b2 Created for SPPU CSE Semester 7 - Laboratory Practice 3