AashishNandakumar

Project Ideas

1. Spam Email Detector

Create a model that classifies emails as spam or not spam. Use machine learning algorithms like Naive Bayes or Support Vector Machines (SVM) to analyze features such as keywords and email formatting. This project helps you understand classification techniques and data preprocessing.

2. Image Classification with ImageNet

Build a convolutional neural network (CNN) to classify images using the ImageNet dataset. This project will introduce you to deep learning frameworks like TensorFlow or PyTorch, and you'll learn about image preprocessing, model training, and evaluation.

3. Personal Assistant Bot

AashishNandakumar

def uppercase_converter(func):
    def wrapper():
        temp_res = func()
        return temp_res.upper()
    return wrapper

# @uppercase_converter
def greet():
    return 'hello world'

AashishNandakumar

#include <iostream>
#include <string>
#include <bitset>

class BinaryManipulation {
public:
    static int minOperations(int n) {
        int operations = 0;
        // Convert the number to its binary representation
        std::string binary = std::bitset<32>(n).to_string();

AashishNandakumar

def getNumPairs(server_nodes, server_from, server_to, server_weight, signal_speed):
    # Step 1: Create an adjacency list to represent the graph
    graph = {i: [] for i in range(1, server_nodes + 1)}
    for i in range(len(server_from)):
        graph[server_from[i]].append((server_to[i], server_weight[i]))
        graph[server_to[i]].append((server_from[i], server_weight[i]))
    
    # Step 2: Implement Depth-First Search (DFS) to find all paths
    def dfs(start, end, path, distance):
        path.append(start)

AashishNandakumar

import 'package:flutter/material.dart';
import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_auth/firebase_auth.dart';
import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:http/http.dart' as http;
import 'dart:convert';

void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  await Firebase.initializeApp();

AashishNandakumar

def logic(N, M, F, B):
    # logic here
    frontend = {}
    backend = {}
    for i in range(N + M):
        frontend[i] = F[i]
        backend[i] = B[i]

    frontend = dict(sorted(frontend.items(), key=lambda x: x[1]))
    backend = dict(sorted(backend.items(), key=lambda x: x[1]))

AashishNandakumar

def klees_super_duper_large_array():
    n, k = map(int, input().split())
    """
    # prefix sum appraoch - O(n) time and O(n) space - gives MLE and TLE
    prefix_sum_array = [0] * n
    for i in range(n):
        prefix_sum_array[i] = k
        if i > 0:
            prefix_sum_array[i] += prefix_sum_array[i - 1]

AashishNandakumar

t = "#" + input()
n = len(t)
longest_valid = ""

for i in range(2, n):
    left_substring = t[1 : i + 1]
    right_substring = t[i : i + len(left_substring)]
    # print(f"left sub: {left_substring}; right sub: {right_substring}")
    if left_substring == right_substring:
        if len(left_substring) > len(longest_valid):

AashishNandakumar

n, k = map(int, input().split())
a = list(map(int, input().split()))

a.sort()
if k == 0:
    min_ele = a[0]
    if min_ele == 1:
        print(-1)
    else:
        print(min_ele - 1)

AashishNandakumar

1. Development of AI/ML-based solution for detection of face-swap-based deep fake videos (SIH1683)

2. Micro-Doppler based Target Classification (SIH1606)

3. AI-powered Student Assistance Chatbot for Department of Technical Education (SIH1631)

4. AI tool/mobile app for Indian Sign Language (ISL) generator from audio-visual content (SIH1715)

5. Detection of Oil Spills at Marine Environment using Automatic Identification System (AIS) and Satellite Datasets (SIH1655)