DAA-ML-BT-Algo

Design and Analysis of Algorithms and Machine Learning code base.

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Algorithm and Machine Learning Projects

This repository contains a collection of algorithms, data structures, and machine learning projects implemented in various programming languages. Each project folder includes code, explanations, and examples. The topics range from fundamental algorithms like Fibonacci and Knapsack problems to machine learning models like K-Nearest Neighbors and K-Means Clustering.

Table of Contents

1. Fibonacci Program (non-recursive and recursive approaches)
2. Huffman Encoding using a Greedy Strategy
3. Fractional Knapsack Problem using a Greedy Algorithm
4. 0-1 Knapsack Problem
5. Quick Sort Analysis using Deterministic Partitioning
6. Uber Ride Price Prediction
7. Email Spam Detection
8. Neural Network-Based Classifier
9. K-Nearest Neighbors Algorithm (Diabetes Dataset)
10. K-Means Clustering (Sales Data Sample)

Project Descriptions

1. Fibonacci Program (non-recursive and recursive approaches)

A classic algorithm to generate Fibonacci numbers, implemented using both recursive and non-recursive (iterative) methods. This project explores the time complexity and performance differences between the two approaches.

2. Huffman Encoding using a Greedy Strategy

Implementation of the Huffman Encoding algorithm, which compresses data by using variable-length codes for characters based on their frequencies. This project uses a greedy approach to build the optimal encoding tree.

3. Fractional Knapsack Problem using a Greedy Algorithm

Solution to the fractional knapsack problem, where items can be divided. The algorithm aims to maximize the total value within a given weight limit by selecting items or fractions of items based on a greedy strategy.

4. 0-1 Knapsack Problem

This project solves the 0-1 Knapsack problem, where items cannot be divided, using dynamic programming. It explores ways to maximize the total value while staying within the weight constraint.

5. Quick Sort Analysis using Deterministic Partitioning

Analysis of the Quick Sort algorithm, using deterministic partitioning. This project explains the partitioning process and compares the efficiency of Quick Sort against other sorting algorithms under various conditions.

6. Uber Ride Price Prediction

A machine learning model to predict the price of Uber rides based on various factors such as distance, time of day, and location. This project demonstrates data preprocessing, feature engineering, and model training for regression tasks.

7. Email Spam Detection

A machine learning classifier to detect spam emails. The project includes data preprocessing, feature extraction (e.g., term frequency-inverse document frequency), and model evaluation metrics for text classification.

8. Neural Network-Based Classifier

Implementation of a neural network classifier for binary or multi-class classification tasks. This project includes training, validation, and tuning of neural networks using a sample dataset.

9. K-Nearest Neighbors Algorithm (Diabetes Dataset)

A K-Nearest Neighbors (KNN) classifier applied to the Diabetes dataset. This project explores the application of KNN for binary classification, parameter tuning, and performance evaluation.

10. K-Means Clustering (Sales Data Sample)

An unsupervised machine learning model using K-Means clustering applied to sales data. This project includes preprocessing, clustering analysis, and visualization of clusters to understand customer segmentation.

How to Run the Projects

Linux (Ubuntu) Terminal Commands

Python

python3 code.py

C++

g++ code.cpp
./a.out

Java

javac code.java
java code

Clone this repository:

git clone https://github.com/Shriharsh-Deshmukh/DAA-ML-BT-Algo.git