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10 Unique C++ Projects with Source Code The C++ programming language is widely recognized for offering numerous advantages compared to the C language. The most interesting feature of this language is its support for object-oriented programming, which opens up a completely new realm of possibilities. We are pleased to provide you with over 10 C++ projects with source code for learners. One of the most popular programming languages that is widely used in many different industries, including games, operating systems, web browsers, and database management systems, is C++. Because it incorporates object-oriented programming concepts, such as the use of specified classes, into the C programming language, some refer to C++ as “C with classes.” Over time, C++ has continued to be a very useful language for computer programming as well as to teach new programmers how object-oriented programming operates. C++ Projects with Source Code 1. Bank Management System Project This project is a simple C++ Project with source code. Bank Management System Project in C++ uses C++ to manage banking operations like account creation, deposits, and withdrawals. Users interact with the system, providing input for tasks such as updating account details. C++ is chosen for its efficiency and versatility, allowing the implementation of features like data processing and user interfaces. The output includes updated account balances and transaction receipts displayed to users. C++ #include <iostream> #include <map> #include <string> using namespace std; class Bank { private: map<string, double> accounts; public: void createAccount(const string& name, double initialDeposit) { if (accounts.find(name) != accounts.end()) { cout << "Account already exists!" << endl; } else { accounts[name] = initialDeposit; cout << "Account created successfully!" << endl; } } void deposit(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { accounts[name] += amount; cout << "Amount deposited successfully!" << endl; } else { cout << "Account does not exist!" << endl; } } void withdraw(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { if (accounts[name] >= amount) { accounts[name] -= amount; cout << "Amount withdrawn successfully!" << endl; } else { cout << "Insufficient balance!" << endl; } } else { cout << "Account does not exist!" << endl; } } void checkBalance(const string& name) { if (accounts.find(name) != accounts.end()) { cout << "Balance for account " << name << " is: " << accounts[name] << endl; } else { cout << "Account does not exist!" << endl; } } }; int main() { Bank bank; // Sample usage bank.createAccount("Alice", 1000); bank.deposit("Alice", 500); bank.checkBalance("Alice"); bank.withdraw("Alice", 200); bank.checkBalance("Alice"); return 0; } #include <iostream> #include <map> #include <string> using namespace std; class Bank { private: map<string, double> accounts; public: void createAccount(const string& name, double initialDeposit) { if (accounts.find(name) != accounts.end()) { cout << "Account already exists!" << endl; } else { accounts[name] = initialDeposit; cout << "Account created successfully!" << endl; } } void deposit(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { accounts[name] += amount; cout << "Amount deposited successfully!" << endl; } else { cout << "Account does not exist!" << endl; } } void withdraw(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { if (accounts[name] >= amount) { accounts[name] -= amount; cout << "Amount withdrawn successfully!" << endl; } else { cout << "Insufficient balance!" << endl; } } else { cout << "Account does not exist!" << endl; } } void checkBalance(const string& name) { if (accounts.find(name) != accounts.end()) { cout << "Balance for account " << name << " is: " << accounts[name] << endl; } else { cout << "Account does not exist!" << endl; } } }; int main() { Bank bank; // Sample usage bank.createAccount("Alice", 1000); bank.deposit("Alice", 500); bank.checkBalance("Alice"); bank.withdraw("Alice", 200); bank.checkBalance("Alice"); return 0; } 2. Train Reservation System Project The concept of the project is a train reservation system to reserve train tickets for multiple destinations. In this project, C++ is used to implement the logic for seat availability, reservation confirmation, and the generation of booking records. C++ is utilized to handle data processing, implement user interfaces, and manage the overall flow of the reservation application. The language allows for implementing key functionalities required for a reservation system, providing users with a straightforward and reliable platform to manage their train travel plans. C++ #include <iostream> #include <map> #include <vector> #include <string> using namespace std; class TrainReservationSystem { private: map<int, int> availableSeats; // Map to store available seats for each train public: TrainReservationSystem() { // Initialize available seats for each train availableSeats[1] = 50; availableSeats[2] = 50; availableSeats[3] = 50; } void displayAvailableSeats() { cout << "Available Seats:" << endl; for (const auto& pair : availableSeats) { cout << "Train " << pair.first << ": " << pair.second << " seats" << endl; } } void bookTicket(int trainNumber, int numTickets) { if (availableSeats.find(trainNumber) != availableSeats.end()) { if (availableSeats[trainNumber] >= numTickets) { availableSeats[trainNumber] -= numTickets; cout << numTickets << " ticket(s) booked successfully for Train " << trainNumber << endl; } else { cout << "Insufficient seats available for Train " << trainNumber << endl; } } else { cout << "Invalid Train Number" << endl; } } void cancelTicket(int trainNumber, int numTickets) { if (availableSeats.find(trainNumber) != availableSeats.end()) { availableSeats[trainNumber] += numTickets; cout << numTickets << " ticket(s) canceled successfully for Train " << trainNumber << endl; } else { cout << "Invalid Train Number" << endl; } } }; int main() { TrainReservationSystem trainSystem; int choice, trainNumber, numTickets; do { cout << "nTrain Reservation System" << endl; cout << "1. Display Available Seats" << endl; cout << "2. Book Ticket" << endl; cout << "3. Cancel Ticket" << endl; cout << "4. Exit" << endl; cout << "Enter your choice: "; cin >> choice; switch(choice) { case 1: trainSystem.displayAvailableSeats(); break; case 2: cout << "Enter Train Number: "; cin >> trainNumber; cout << "Enter Number of Tickets: "; cin >> numTickets; trainSystem.bookTicket(trainNumber, numTickets); break; case 3: cout << "Enter Train Number: "; cin >> trainNumber; cout << "Enter Number of Tickets: "; cin >> numTickets; trainSystem.cancelTicket(trainNumber, numTickets); break; case 4: cout

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Limited Resources One of the major drawbacks of free hosting is the limited resources available to you. Free hosting providers frequently limit bandwidth, storage space, and other critical resources. If your website is within these limits, it may experience slowdowns or failure. Lack of Customization Options Free hosting often offers fewer customisation possibilities than premium hosting solutions. You may be unable to access advanced features or install custom applications or plugins. This may limit your ability to personalise your website based on your preferences and requirements. Limited Customer Support Free hosting may provide a different level of customer support than paid hosting plans. While certain companies offer basic help through email or forums, response times may be slow, and support quality may need improvement. This can be irritating if you have technical difficulties or require support troubleshooting problems with your website. While free hosting is a cost-effective choice, measuring the downsides against the benefits before selecting is essential. When choosing a hosting service, consider your website’s requirements, long-term goals, and budget limits. In some cases, investing in a paid hosting package may improve website reliability, performance, and support in the long term. Can I host a WordPress site for free at 000webhost? Yes, you can host a WordPress site for free with 000webhost. They offer free hosting and one-click WordPress installation. Is there a limit to the number of websites I can host for free on 000webhost? Yes, there is a limit to the number of websites you can host for free on 000webhost. The free plan allows you to host one website per account. Does 000webhost offer SSL certificates for free? Yes, 000webhost provides free SSL certificates to all websites hosted on its platform. SSL encryption ensures that data transferred between your website and visitors’ browsers is secure. Can I upgrade to a paid plan on 000webhost? Yes, 000webhost offers paid plans with additional features and resources for those who require more advanced hosting solutions. You can upgrade your plan at any time to unlock premium features. Final Year Projects Data Science Projects Blockchain Projects Python Projects Cyber Security Projects Web dev Projects IOT Projects C++ Projects Top 20 Machine Learning Project Ideas for Final Years with Code 10 Deep Learning Projects for Final Year in 2024 Realtime Object Detection 10 Advance Final Year Project

Best 20 artificial intelligence projects for final year As a computer science student, you may question how to leave a long-term mark on your final year project. Selecting a project in the final year is important for your grades and future life. Artificial intelligence projects for last year can help you get more chances and improve your resume. Artificial Intelligence is a dynamic and evolving field that offers various possibilities. In this article, we will explore a variety of innovative and interesting Artificial Intelligence projects for the final year. What is artificial Intelligence? When many people hear the term artificial intelligence, they usually think of robots. But artificial intelligence has almost every element of our lives, from voice assistants to independent vehicles. Artificial Intelligence is also defined   An Intelligent Being Made by Mankind Able to carry out tasks wisely, even without clear instructions. Able to reason and behave humanely and reasonably.In your final year, diving into the world of AI allows you to add to this rapidly developing area. AI projects display technological expertise and show your ability to deal with real-world problems. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Artificial Intelligence projects for final year with source code 1. Augmented Reality- Based Learning Platform For Distance Education The first artificial intelligence project for the final year is an augmented reality-based learning platform. Building a digital environment where students utilize gadgets to access interactive instructional information is the first step in creating a raised reality-based learning platform for distance learning. Through increased reality capabilities, the platform improves student learning by enabling them to visualize concepts and participate in virtual activities. Source Code 2. Development of a low cost Air pollution Monitoring System  A Low-Cost Air Pollution Monitoring System is designing a solution that allows easy and affordable air quality tracking. In this system, input comes from sensors that measure various pollutants in the air, such as particulate matter and gases. These sensors are placed strategically in different locations. The data collected is then processed and sent to a central system. The output is accessible information about air quality levels, which can be displayed on a user-friendly interface or made available through a mobile app. This project aims to provide an affordable and efficient way for communities to monitor their air quality, enabling individuals to make informed decisions about their health and environment.   Research Paper 3. Real Time Video Analytics and Object Detection This project is a very simple artificial intelligence project for the final year. Real-time video Analytics and Object Detection systems involve developing a technology that instantly analyzes videos and identifies objects within them. In this system, the input consists of video feeds from cameras or other sources, and advanced algorithms process this information in real time. The system can detect and recognize objects, people, or events within the video frames. The output is valuable insights and data, such as identifying specific objects or tracking movement patterns. Source Code 4. Design and implementation of a Smart Home Using Artificial Intelligence It aims to make homes more convenient and energy efficient while providing users with an easy-to-use and adaptable home. A Smart Home using Artificial Intelligence involves creating a system where everyday devices are connected and automated through intelligent algorithms. In this setup, the input comes from various smart devices like thermostats, lights, and sensors, which can be controlled and monitored through a central hub. The system uses artificial intelligence to learn user preferences and adjust their routines. The output is a seamlessly automated home environment where AI algorithms manage tasks such as changing temperature, controlling lighting, and predicting user needs. Source Code 5. Development of Real Time Traffic Monitoring System It helps cities manage traffic flow, reduce congestion, and enhance overall transportation efficiency. This system involves creating a solution that continuously observes and analyzes traffic conditions. This system’s input consists of data from various sources, such as cameras, sensors, and GPS devices, capturing real-time information about vehicle movements and congestion. The system processes this data instantly to generate useful output, such as live traffic updates, congestion alerts, and suggested alternate routes. This design aims to provide commuters with timely and accurate information, allowing them to make informed decisions about their travel routes. Source Code 6. Design and Implementation Of an automated Parking System This artificial intelligence for the final year project can simplify and optimize parking, reducing the time and effort required to find a parking spot. Designing and implementing an Automated Parking System involves creating a solution where parking vehicles are streamlined and automated. This system’s input comes from sensors and cameras that monitor available parking spaces. As a car approaches, the system processes this data to guide the driver to an open parking spot. The output is an automated parking experience, where the system controls entry barriers, shows the driver available space, and manages the parking process. Source Code 7. Design and Implementation of Autonomous Drone System An Autonomous Drone System involves creating a technology where drones can operate independently with minimal human intervention. In this system, the input comes from pre-programmed instructions, GPS coordinates, or real-time data from sensors on the drone. The system processes this information to navigate, avoid obstacles, and perform tasks. The output is the autonomous operation of the drone, capable of tasks such as surveillance, package delivery, or capturing aerial imagery. Source Code 8. Artificial Intelligence In Health Care It aims to utilize artificial intelligence to improve healthcare procedures, enhance diagnostics, and ultimately improve patient results. This System involves incorporating smart technology to enhance medical processes. This System gathers input from patient records, medical imaging, and real-time monitoring devices. Artificial Intelligence processes this information, providing valuable insights such as personalized treatment recommendations, early disease detection, and efficient management of medical records. The output is a more efficient and adaptive healthcare system that assists medical professionals in making informed decisions and delivering improved patient care.

Plant Disease Detection using Machine Learning with Source Code Ensuring that crops are healthy and full of nutrients is very important for long-term food production. However, diseases that affect plant leaves can slow growth and yield, a problem for farms worldwide. Conventional methods for finding diseases often involve inspecting things by hand, which can take a long time and lead to mistakes. Here comes machine learning, which has changed the game in agriculture by creating new ways to quickly and correctly find leaf diseases. Understanding Leaf Diseases Before getting into the details of machine learning-based detection, it’s important to understand the basics of leaf diseases. These diseases include a variety of fungal, bacterial, and viral infections, as well as physiological defects, all of which cause unique symptoms on the leaves of plants. From white mold to bacterial disease, each disease has a distinct appearance, making early detection difficult for farmers. The Limitations of Traditional Methods In the past, farmers had to look at leaves by hand to find leaf diseases. This method worked to some extent, but it takes a lot of work and is easy to overlook. Also, how different people see things can make diagnoses inconsistent. More effective ways to find diseases are needed because farming areas are growing, and diseases are becoming more complicated. Enter Machine Learning Traditional methods of finding diseases have drawbacks that machine learning systems could help fix. These algorithms can accurately tell the difference between different signs by using huge datasets containing images of healthy and sick leaves. Machine learning models can quickly find signs of disease through pattern recognition and classification, which lets people take action before they get infected. Training the Algorithm The functioning of machine learning-based detection systems depends on the algorithm’s training. This involves adding labelled images of healthy and diseased leaves into the model, allowing it to gain knowledge of each state’s unique characteristics. With increased data analysis iteration, the algorithm shows enhanced precision in classifying leaves images, which creates a foundation for strong disease detection. Image Preprocessing Techniques Preprocessing techniques extract important information and improve image quality before feeding the images into the machine-learning model. Resizing, normalization, and augmentation are a few strategies used to standardize the input data and enhance the algorithm’s performance. The dataset is refined through preprocessing so that the model can concentrate on identifying significant patterns in the images. Real-Time Detection and Monitoring The best thing about plant disease detection using machine learning is that it can monitor crop health in real-time. With sensors and recording devices built in, these systems can constantly look over fields for signs of disease and inform farmers immediately about any possible threats. This proactive method allows people to step in quickly, reducing crop losses and increasing yields. Scalability and Adaptability Another significant advantage of machine learning in agriculture is its scalability and adaptation across different crops and conditions. Like previous approaches, which may be limited to certain diseases or plant species, machine learning algorithms can be trained to detect a wide range of leaf diseases. Furthermore, these models can adjust to changing disease patterns, ensuring their value in dynamic agricultural settings. Challenges and Considerations Even though machine learning has a lot of potential for detecting leaf diseases, there are certain difficulties. Some aspects that need to be carefully considered are data availability, model interpretability, and implementation costs. Another major challenge is guaranteeing the algorithms’ durability and generalizability under various growing situations. Plant Disease Detection Source Code To create a plant disease detection in Python. First, download the dataset from here and then follow these steps: 1. Importing Libraries The required libraries, including matplotlib, torch, torchvision, pandas, and numpy, are imported into this block. Additionally, it imports particular modules from torchvision, including the SubsetRandomSampler from torch.utils.data.sampler and datasets, transformations, models, nn, and functional. import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch from torchvision import datasets, transforms, models from torch.utils.data.sampler import SubsetRandomSampler import torch.nn as nn import torch.nn.functional as F from datetime import datetime import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch from torchvision import datasets, transforms, models from torch.utils.data.sampler import SubsetRandomSampler import torch.nn as nn import torch.nn.functional as F from datetime import datetime 2. Defining Image Transformations This block defines a set of image preprocessing transformations that use torchvision transforms. Compose. These changes include scaling, center cropping, and turning photos into tensors. transform = transforms.Compose( [transforms.Resize(255), transforms.CenterCrop(224), transforms.ToTensor()] ) transform = transforms.Compose( [transforms.Resize(255), transforms.CenterCrop(224), transforms.ToTensor()] ) 3. Loading Dataset This block loads images from a folder (“dataset_images”) using torchvision’s datasets.ImageFolder and applies the defined transformations. dataset = datasets.ImageFolder("dataset_images", transform=transform) dataset = datasets.ImageFolder("dataset_images", transform=transform) 4.Splitting Dataset Indices This block randomly shuffles the indices of the dataset and splits them into train, validation, and test indices based on predefined proportions. indices = list(range(len(dataset))) split = int(np.floor(0.85 * len(dataset))) validation = int(np.floor(0.70 * split)) train_indices, validation_indices, test_indices = ( indices[:validation], indices[validation:split], indices[split:], ) indices = list(range(len(dataset))) split = int(np.floor(0.85 * len(dataset))) validation = int(np.floor(0.70 * split)) train_indices, validation_indices, test_indices = ( indices[:validation], indices[validation:split], indices[split:], ) 5. Defining Model Architecture This block defines a CNN class inheriting from nn.Module, specifying the architecture with convolutional and dense layers. class CNN(nn.Module): def __init__(self, K): super(CNN, self).__init__() self.conv_layers = nn.Sequential( # conv1 nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.MaxPool2d(2), # conv2 nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.MaxPool2d(2), # conv3 nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(128), nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(128), nn.MaxPool2d(2), # conv4 nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(256), nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(256), nn.MaxPool2d(2), ) self.dense_layers = nn.Sequential( nn.Dropout(0.4), nn.Linear(50176, 1024), nn.ReLU(), nn.Dropout(0.4), nn.Linear(1024, K), ) def forward(self, X): out = self.conv_layers(X) # Flatten out = out.view(-1, 50176) # Fully connected out = self.dense_layers(out) return out class CNN(nn.Module): def __init__(self, K): super(CNN, self).__init__() self.conv_layers = nn.Sequential( # conv1 nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.MaxPool2d(2), # conv2 nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3,

15 Exciting blockchain project Ideas with Source Code Blockchain Project ideas world where innovation and decentralization converge to reshape industries. Blockchain project technology, originally designed to support cryptocurrencies like Bitcoin, has evolved into a versatile tool with the potential to revolutionize various sectors. I am going to present 15 creative blockchain project ideas for the final year project and also to gain the supervisor’s approval for your final-year project. What is Block chain? Blockchain technology is a decentralized and distributed ledger that records transactions across multiple computers. Each transaction is secured using cryptography, creating a tamper-resistant and transparent system. The basic concept involves a chain of blocks, where each block contains a list of transactions linked together through cryptographic hashes. 15 Block Chain Project Ideas with Source Code 1. Block chain project in Healthcare Data Management The healthcare industry struggles with enormous volumes of private patient information, but it’s often spread out in different places. In this project, blockchain can enhance data security by creating a decentralized and secure ledger for storing patient records. This system ensures privacy, interoperability, and transparency in managing sensitive medical information. In this system, the chain ensures patients have control over who accesses their data and for what purpose. This transparency in data usage fosters trust between patients and healthcare providers, addressing concerns about privacy breaches. Source Code 2. Block chain voting system The next one is the blockchain voting system. This blockchain project is one of the most impactful and can be a good project for the final year. Creating this system using advanced technology to fix problems in traditional voting, like fraud. Using a tamper-proof digital ledger called a blockchain makes voting secure and clear. We pick a good blockchain platform and build smart contracts that define who can vote and how. The voting interface is made simple for everyone to use. A solid system verifies and records votes on the blockchain, ensuring they can’t be changed. The results are shown openly for trust. Security measures, like encryption, protect against attacks. Thorough testing, deploying on a reliable blockchain, and teaching users ensure a trustworthy system. Continuous monitoring, improvements, and following laws keep the Blockchain Voting System working well over time. Source Code Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning 3. Decentralized File Storage The next blockchain project idea is decentralized file storage. A decentralized file storage system involves creating a system where files are distributed across multiple nodes, ensuring reliability, security, and scalability. In such a system, users input their files into smaller pieces or encrypted chunks. These fragments are distributed across various nodes in the network, eliminating the need for a central server. The decentralized nature ensures no single point of failure exists, providing robustness and reducing the risk of data loss. When users want to retrieve their files, the system locates and assembles the necessary fragments from different nodes, reconstructing the original data. This approach enhances data privacy and security and promotes scalability, as the system can efficiently handle a growing volume of information. Source Code 4. Block chain powered prediction market A blockchain-powered prediction market system involves building a platform where people can predict outcomes and make bets using blockchain technology. In this system, users input predictions or bets, and the blockchain records these transactions securely. The design ensures the information is decentralized and cannot be tampered with. When an event occurs, the blockchain automatically executes and settles the bets based on the outcome. Users can then easily retrieve their winnings directly through the blockchain. This decentralized approach removes the need for a middleman, making the prediction market more transparent and resistant to manipulation. It also allows people from anywhere to participate, promoting a global and inclusive prediction market. Source Code 5. Decentralized Autonomous Vehicles Decentralized Autonomous Vehicles (DAV) system involves creating a network where vehicles can operate independently without a central authority. In this system, input comes from the cars, which use sensors and data to make decisions about their movements and actions. Each vehicle is like a smart robot on the road, constantly communicating with others to navigate safely. The output is a coordinated traffic system where vehicles work together, sharing information through a decentralized network powered by blockchain. This design allows vehicles to make decisions collectively, helping avoid accidents and optimize traffic flow. Moreover, it ensures no single control centre is needed, making the system more resilient to failures or attacks. The DAV system aims to create a safer, more efficient, and decentralized way for autonomous vehicles to operate on the roads. Source Code 6. Block Chain based Supply Chain Financing This blockchain project idea is very unique. This system involves creating a digital platform that helps businesses in the supply chain get financing more easily. In this system, enterprises input information about their transactions and supply chain activities onto the blockchain. This input could include details about the products, shipments, and payments. The blockchain securely records and verifies these transactions, creating a transparent and unchangeable history. The system’s output is that this recorded information can be used to secure financing. Banks or lenders can trust the data on the blockchain, making it easier for businesses to get loans or other financial support based on their reliable and transparent supply chain activities. This approach reduces the risk of fraud and provides a more efficient and accessible way for businesses in the supply chain to access the financing they need to grow and operate smoothly. Source Code 7. Block Chain Based gaming Blockchain-based gaming system involves creating a platform where players can enjoy games with added security, transparency, and unique ownership features. In this system, input comes from players who use the blockchain to buy, sell, and trade in-game items or characters. The blockchain records these transactions securely, making them verifiable and tamper-resistant. The output is a gaming environment where players truly own their in-game assets. Because of the blockchain’s decentralized nature,

Best 20 data science projects with source code You have your sights set on an attractive career in data science. You know that you have the data science skills needed for the position. The issue is that you need more proof to support your data science skills. Anybody can claim to be a talented data scientist on their resume, but hiring managers will want evidence to support this claim. However, how can you show hiring managers that you’re worth their time by standing out like a perfect data scientist? Data science tasks are simple. Putting things into practice is the most efficient method! Why Data Science Projects Are Important for a Successful Career in Data Science? With IBM estimating 700,000 job openings by the end of 2020, data science is—and will always be—the hottest career choice, with demand for data professionals increasing steadily as the market expands. It takes an average of 60 days to fill an open data science position and 70 days to fill a senior data scientist position. CEOs and hiring managers at leading tech businesses aim for data scientists who can solve real-world challenges and connect their work to commercial value. Companies now employ people based on their ability to use data science rather than just academic knowledge. The greatest method to learn data science and develop a practical skill set is to begin working on data science projects. Several years ago, most data science positions required a Master’s or Ph.D. in Mathematics or statistics. However, in recent years, things have changed. The massive skills gap and the growth of data science careers have encouraged businesses to hire people who can add value to a company as quickly as feasible. Only by working with popular data science projects and completing several interesting projects will you learn how data architectures work in practice.Also, as more organizations shift their machine learning solutions and data to the cloud, data scientists must master various associated tools and technologies to stay updated. Unlocking the World of Data Science Projects: 20 Gems with Source Code Data science is more than a word; it is an exciting field that allows us to extract useful information from the huge ocean of data. Whether you’re an experienced data scientist or a newbie, getting involved in practical projects is the greatest way to polish your abilities and remain current on the latest trends. This article will examine 20 interesting data science projects using source code, providing a hands-on approach to mastering this exciting study area. 1. Price Recommendation for Online Sellers Many machine learning algorithms power a lot of e-commerce sites. These algorithms do everything from checking the quality of the products and managing the inventory to finding out who is buying what and suggesting products. E-commerce websites and apps are also trying to find ways to make it so that humans don’t have to make price ideas for sellers on their marketplace. This is another interesting business use case that they are trying to solve. That’s where machine learning for price prediction comes in. You can use your data science skills on various datasets and in interesting projects to solve hard, real-world data science problems. As part of this data science project, you will create a machine learning model to help online sellers find the best product prices. Different items with very small differences, like extra features or brand names, can have different prices depending on how much people want them. This is a difficult data science problem. It’s even harder to use price prediction models when there are millions of goods, which is the case with most eCommerce platforms. Source Code 2. Walmart Store’s Sales Forecasting Big data and data science are used by e-commerce and retail to streamline business operations and make profitable decisions. Data science techniques are used to effectively handle a variety of functions, including inventory management, product recommendations to customers, and sales prediction. Walmart generated $482.13 billion in revenue in 2016 thanks to accurate estimates across its 11,500 locations through data science approaches. The name of this data science project makes it obvious that you will be working with a dataset of 143 weeks’ worth of sales transaction records from 45 Walmart shops and their 99 divisions. Predicting future sales across many departments inside different Walmart shops is an interesting data science topic. This data science project’s most challenging aspect is predicting sales for the four biggest holidays: Labour Day, Christmas, Thanksgiving, and the Super Bowl. Walmart forecasts sales for these events to ensure enough product supply to satisfy demand. Markdown discounts, the consumer price index, whether the week was a holiday, weather, store size, store type, and unemployment rate are just a few details in the dataset. Source Code Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning 3. Personalized Medicine Recommending System Recently, there has been much discussion among cancer researchers about how using genetic testing to treat diseases like cancer may revolutionize the field of cancer research. Clinical pathologists have made huge contributions that have helped bring this ideal revolution to fruition. The pathologist manually deduces the meaning of genetic alterations after initially sequencing a gene related to a malignant tumor. The pathologist must look for evidence in clinical literature to generate interpretations, which is time-consuming and laborious. But we can streamline this process by putting machine learning algorithms into practice. This project will be a good starting point for you to study the field of medicine and artificial intelligence integration. With the data from the Memorial Sloan Kettering Cancer Centre (MSKCC) dataset, automate the classification of every genetic mutation found in the cancer tumor. The collection includes neutral mutations (passengers) and mutations classified as drivers of cancer growth. Reputable scientists and oncologists have manually marked the dataset. Use the MSKCC dataset to create an automated system that can categorize genetic mutations in cancer tumors into classes of drivers and passengers. Source Code 4.

10 Best C++ Projects for Beginners Are you a beginner looking to sharpen your skills in C++? Look no further! In this article, we’ll explore 10 C++ projects for beginners that help you learn the fundamentals of the language and introduce you to the world of software development. The source code is also available to help beginners. 10 Best C++ Projects for Beginners with Source Code 1.Book Shop Management System in C++ You can build a management system for a bookshop. With C++, you can create a complete management system that handles inventory, sales tracking, and customer information tasks. By executing concepts like classes, objects, and file handling, you’ll gain a deeper understanding of C++ fundamentals while developing a practical solution for real-world businesses.   Source Code for Book Shop Management System in C++ C++ #include <iostream> #include <vector> #include <string> using namespace std; // Define a structure to represent a book struct Book { string title; string author; int quantity; double price; }; // Function to add a book to the inventory void addBook(vector<Book>& inventory) { Book newBook; cout << "Enter book title: "; getline(cin >> ws, newBook.title); // Using getline to handle spaces in book titles cout << "Enter author name: "; getline(cin >> ws, newBook.author); cout << "Enter quantity: "; cin >> newBook.quantity; cout << "Enter price: "; cin >> newBook.price; inventory.push_back(newBook); cout << "Book added successfully!" << endl; } // Function to display all books in the inventory void displayBooks(const vector<Book>& inventory) { cout << "Inventory:" << endl; for (const auto& book : inventory) { cout << "Title: " << book.title << ", Author: " << book.author << ", Quantity: " << book.quantity << ", Price: $" << book.price << endl; } } // Function to search for a book by title void searchBook(const vector<Book>& inventory, const string& title) { bool found = false; for (const auto& book : inventory) { if (book.title == title) { cout << "Book found:" << endl; cout << "Title: " << book.title << ", Author: " << book.author << ", Quantity: " << book.quantity << ", Price: $" << book.price << endl; found = true; break; } } if (!found) { cout << "Book not found." << endl; } } int main() { vector<Book> inventory; int choice; string searchTitle; do { cout << "nBookshop Management Systemn"; cout << "1. Add a bookn"; cout << "2. Display all booksn"; cout << "3. Search for a book by titlen"; cout << "4. Exitn"; cout << "Enter your choice: "; cin >> choice; switch(choice) { case 1: cin.ignore(); // Ignore the newline character left by cin addBook(inventory); break; case 2: displayBooks(inventory); break; case 3: cout << "Enter book title to search: "; cin.ignore(); // Ignore the newline character left by cin getline(cin >> ws, searchTitle); searchBook(inventory, searchTitle); break; case 4: cout << "Exiting…"; break; default: cout << "Invalid choice. Please enter a number from 1 to 4." << endl; } } while (choice != 4); return 0; } #include <iostream> #include <vector> #include <string> using namespace std; // Define a structure to represent a book struct Book { string title; string author; int quantity; double price; }; // Function to add a book to the inventory void addBook(vector<Book>& inventory) { Book newBook; cout << "Enter book title: "; getline(cin >> ws, newBook.title); // Using getline to handle spaces in book titles cout << "Enter author name: "; getline(cin >> ws, newBook.author); cout << "Enter quantity: "; cin >> newBook.quantity; cout << "Enter price: "; cin >> newBook.price; inventory.push_back(newBook); cout << "Book added successfully!" << endl; } // Function to display all books in the inventory void displayBooks(const vector<Book>& inventory) { cout << "Inventory:" << endl; for (const auto& book : inventory) { cout << "Title: " << book.title << ", Author: " << book.author << ", Quantity: " << book.quantity << ", Price: $" << book.price << endl; } } // Function to search for a book by title void searchBook(const vector<Book>& inventory, const string& title) { bool found = false; for (const auto& book : inventory) { if (book.title == title) { cout << "Book found:" << endl; cout << "Title: " << book.title << ", Author: " << book.author << ", Quantity: " << book.quantity << ", Price: $" << book.price << endl; found = true; break; } } if (!found) { cout << "Book not found." << endl; } } int main() { vector<Book> inventory; int choice; string searchTitle; do { cout << "nBookshop Management Systemn"; cout << "1. Add a bookn"; cout << "2. Display all booksn"; cout << "3. Search for a book by titlen"; cout << "4. Exitn"; cout << "Enter your choice: "; cin >> choice; switch(choice) { case 1: cin.ignore(); // Ignore the newline character left by cin addBook(inventory); break; case 2: displayBooks(inventory); break; case 3: cout << "Enter book title to search: "; cin.ignore(); // Ignore the newline character left by cin getline(cin >> ws, searchTitle); searchBook(inventory, searchTitle); break; case 4: cout << "Exiting…"; break; default: cout << "Invalid choice. Please enter a number from 1 to 4." << endl; } } while (choice != 4); return 0; } 2. Banking Management system The banking management system can be a good C++ project for beginners. This project lets you simulate essential banking operations like account creation, deposits, withdrawals, and balance inquiries. Not only does this project enhance your C++ proficiency, but it also introduces you to the importance of error handling and exception management in software development. Source Code for Banking Management system in C++ C++ #include <iostream> #include <vector> #include <iomanip> using namespace std; // Define a structure to represent an account struct Account { int accountNumber; string accountHolderName; double balance; }; // Function to create a new account void createAccount(vector<Account>& accounts) { Account newAccount; cout << "Enter account number: "; cin >> newAccount.accountNumber; cout << "Enter account holder name: "; cin.ignore(); // Ignore the newline character left by cin getline(cin, newAccount.accountHolderName); cout << "Enter initial balance: "; cin >> newAccount.balance; accounts.push_back(newAccount);

Best 20 python projects for beginners with source code Are you an aspiring Python programmer looking for creative projects to sharpen your skills? Whether you’re a beginner or have some coding experience, practicing on projects is a fantastic way to support your learning and apply your knowledge to real-world scenarios. Below, we have combined 20 Python projects with source code specifically for beginners. Let’s dip in and explore each idea in detail. 1. To-Do List Application Create a command-line or GUI-based to-do list application using Python. Users should be able to add, delete, update, and view tasks, with options to mark tasks as completed. Pyton # Define an empty list to store tasks tasks = [] # Function to add a new task def add_task(): task = input("Enter the task: ") tasks.append({"task": task, "completed": False}) print("Task added successfully!") # Function to view all tasks def view_tasks(): if not tasks: print("No tasks found.") else: print("Tasks:") for idx, task in enumerate(tasks, start=1): status = "Done" if task["completed"] else "Not Done" print(f"{idx}. {task['task']} – {status}") # Function to mark a task as completed def mark_completed(): view_tasks() idx = int(input("Enter the task number to mark as completed: ")) – 1 if 0 <= idx < len(tasks): tasks[idx]["completed"] = True print("Task marked as completed!") else: print("Invalid task number.") # Function to delete a task def delete_task(): view_tasks() idx = int(input("Enter the task number to delete: ")) – 1 if 0 <= idx < len(tasks): del tasks[idx] print("Task deleted successfully!") else: print("Invalid task number.") # Main function to run the To-Do List Application def main(): while True: print("nTo-Do List Application") print("1. Add Task") print("2. View Tasks") print("3. Mark Task as Completed") print("4. Delete Task") print("5. Exit") choice = input("Enter your choice (1-5): ") if choice == "1": add_task() elif choice == "2": view_tasks() elif choice == "3": mark_completed() elif choice == "4": delete_task() elif choice == "5": print("Thank you for using the To-Do List Application. Goodbye!") break else: print("Invalid choice. Please enter a number from 1 to 5.") if __name__ == "__main__": main() # Define an empty list to store tasks tasks = [] # Function to add a new task def add_task(): task = input("Enter the task: ") tasks.append({"task": task, "completed": False}) print("Task added successfully!") # Function to view all tasks def view_tasks(): if not tasks: print("No tasks found.") else: print("Tasks:") for idx, task in enumerate(tasks, start=1): status = "Done" if task["completed"] else "Not Done" print(f"{idx}. {task['task']} – {status}") # Function to mark a task as completed def mark_completed(): view_tasks() idx = int(input("Enter the task number to mark as completed: ")) – 1 if 0 <= idx < len(tasks): tasks[idx]["completed"] = True print("Task marked as completed!") else: print("Invalid task number.") # Function to delete a task def delete_task(): view_tasks() idx = int(input("Enter the task number to delete: ")) – 1 if 0 <= idx < len(tasks): del tasks[idx] print("Task deleted successfully!") else: print("Invalid task number.") # Main function to run the To-Do List Application def main(): while True: print("nTo-Do List Application") print("1. Add Task") print("2. View Tasks") print("3. Mark Task as Completed") print("4. Delete Task") print("5. Exit") choice = input("Enter your choice (1-5): ") if choice == "1": add_task() elif choice == "2": view_tasks() elif choice == "3": mark_completed() elif choice == "4": delete_task() elif choice == "5": print("Thank you for using the To-Do List Application. Goodbye!") break else: print("Invalid choice. Please enter a number from 1 to 5.") if __name__ == "__main__": main() 2. Weather App Develop a weather application that fetches data from a weather API and displays current weather conditions, forecasts, and temperature trends for a specified location. Pyton import requests def get_weather(city): # Replace 'YOUR_API_KEY' with your actual OpenWeatherMap API key api_key = 'YOUR_API_KEY' url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric' response = requests.get(url) data = response.json() if data['cod'] == 200: weather = { 'city': data['name'], 'temperature': data['main']['temp'], 'description': data['weather'][0]['description'], 'humidity': data['main']['humidity'], 'wind_speed': data['wind']['speed'] } return weather else: return None def main(): print("Welcome to the Weather App!") city = input("Enter the city name: ") weather = get_weather(city) if weather: print(f"Weather in {weather['city']}:") print(f"Temperature: {weather['temperature']}°C") print(f"Description: {weather['description']}") print(f"Humidity: {weather['humidity']}%") print(f"Wind Speed: {weather['wind_speed']} m/s") else: print("Weather data not available for the entered city.") if __name__ == "__main__": main() import requests def get_weather(city): # Replace 'YOUR_API_KEY' with your actual OpenWeatherMap API key api_key = 'YOUR_API_KEY' url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric' response = requests.get(url) data = response.json() if data['cod'] == 200: weather = { 'city': data['name'], 'temperature': data['main']['temp'], 'description': data['weather'][0]['description'], 'humidity': data['main']['humidity'], 'wind_speed': data['wind']['speed'] } return weather else: return None def main(): print("Welcome to the Weather App!") city = input("Enter the city name: ") weather = get_weather(city) if weather: print(f"Weather in {weather['city']}:") print(f"Temperature: {weather['temperature']}°C") print(f"Description: {weather['description']}") print(f"Humidity: {weather['humidity']}%") print(f"Wind Speed: {weather['wind_speed']} m/s") else: print("Weather data not available for the entered city.") if __name__ == "__main__": main() 3. Recipe Finder Design a recipe finder application that retrieves recipes based on user preferences or ingredients. Integrate with a recipe API to fetch recipe details and display them to the user. Pyton import requests def find_recipes(query): # Replace 'YOUR_API_KEY' with your actual Spoonacular API key api_key = 'YOUR_API_KEY' url = f'https://api.spoonacular.com/recipes/complexSearch?apiKey={api_key}&query={query}&number=5' response = requests.get(url) data = response.json() if 'results' in data: recipes = [] for result in data['results']: recipe = { 'title': result['title'], 'url': result['sourceUrl'] } recipes.append(recipe) return recipes else: return None def main(): print("Welcome to the Recipe Finder!") query = input("Enter an ingredient or dish name: ") recipes = find_recipes(query) if recipes: print(f"Here are some recipes containing '{query}':") for idx, recipe in enumerate(recipes, start=1): print(f"{idx}. {recipe['title']} – {recipe['url']}") else: print("No recipes found for the entered query.") if __name__ == "__main__": main() import requests def find_recipes(query): # Replace 'YOUR_API_KEY' with your actual Spoonacular API key api_key = 'YOUR_API_KEY' url = f'https://api.spoonacular.com/recipes/complexSearch?apiKey={api_key}&query={query}&number=5' response = requests.get(url) data = response.json() if 'results' in data: recipes = [] for result in data['results']: recipe = { 'title': result['title'], 'url': result['sourceUrl'] } recipes.append(recipe) return recipes else: return None def main(): print("Welcome to the Recipe Finder!") query = input("Enter an ingredient or dish name: ") recipes = find_recipes(query) if recipes: print(f"Here are some recipes containing '{query}':") for idx, recipe in

30 Final Year Project Ideas for IT Students Final Year is critical in our college and university life. Because we have to showcase all that we have learnt in our bachelor’s or master’s degrees, the showcase may be a thesis or final year project. As IT students, we must make a final year project to complete our college or university degree. The final Year project is a difficult task as selecting an idea, then documentation, and then the final year project. But in this article I have compiled 30 Creative final year project ideas. These ideas are unique and advanced. I can bet this article will make your final Year easy and chill. So, let’s dive in and explore these 30 IT projects. 30 Final Year Project Ideas for IT Students with Source Code 1. Learning Management System In my list, the first one is the learning management system. This management system facilitates all the stakeholders in an educational institution, such as students, teachers and administration. Managing many students and staff is a difficult task. In the past bookkeeper kept a records of students’ fees, attendance, teachers’ salary, and admin staff. However, it is difficult for a bookkeeper to manage all the stakeholders and facilities. As technology evolves daily, AI is revolutionizing the world, so we must change our traditional system and bring technology to our organizations and institutions. But you may think this project was done by our super seniors five years ago, and our university is also using LMS, so why should you develop this project? I am not saying you that create a simple LMS that your university already uses. Introduce new AI features such as Personalized Learning Paths, Adaptive Learning, Intelligent Content Recommendations, Automated Grading and Feedback and Predictive Analytics for Student Success. It is not necessary to build a new system every time. Sometimes, you can improve the existing one. Source Code Recommended Reading AI Music Composer using Machine Learning Real-Time Object Detection Using Machine Learning 30 Creative Final Year Projects with Source Code 2. Housing Management System Many houses are in a society, and managing them effectively is very important. The best project is that which helps people to solve their problems. This project helps house owners and society admin manage all the complaints, bills and selling their houses. The main feature of this project is the complaint system because there can be multiple complaints like security, water, laundry, etc. The housing management system can help society owners solve housing issues in society. Using AI in this system can be effective such as generating automated bills, Predictive maintenance and Energy management. This project can be a good choice for the final year. So what are you waiting for? Select this idea and get approval from your supervisor. Next, download the source code, modify it, and design it according to your choice. Source Code 3. Smart Security App Security concerns are increasing in many countries. It is necessary to use technology to tackle security concerns. You can develop an intelligent security app which automatically identifies threats from your surroundings. I have an answer if you are thinking about how an app can detect a threat. Sensors can play an essential role in this project. You can connect the sensor to your app. Suppose that when you feel threatened, you don’t have time to call anyone in a tense situation. You simply double-tap on your mobile, and the threat message is automatically sent to the nearby police station. Same as this, you can use another sensor to inform nearby threats. For the final year project, you can develop an intelligent security solution using AI and IoT that interacts with new threats. Source Code 4. E-commerce Website E-commerce means selling products via the Internet, the fastest-growing field in the world. But to sell products online, you need an E-commerce website. Making an ecommerce website is not a simple task. Nowadays, people are developing ecommerce websites on WordPress or Shopify, but creating an ecommerce website is challenging, and you can take this challenge for a final-year project. You can make this project in different programming languages such as HTML, CSS, JAVASCRIPT and PHP. You can also use the MERN stack to create an Ecommerce website. Make a beautiful ecommerce website and impress your supervisor. After your degree, you can also provide Ecommerce website development services and earn money. It means killing two birds with one stone. So work smart, not hard. Source Code 5. Smart Traffic System Traffic is a critical concern in big cities. And it affects millions of people worldwide. To solve this issue, you can develop an intelligent traffic management system that uses AI to manage the traffic system and decrease congestion intelligently. The Smart Traffic System uses data analytics, sensor technologies, and predictive algorithms to improve traffic flow, reduce congestion, and increase road safety. You can also attach it to Google Maps API to improve its accuracy and flow. By creating such a system, you can help to make cities more intelligent and sustainable, focusing on efficiency and mobility. Source Code 6. Tourism Guide System Many countries, such as Maldives, Jamaica and Sri Lanka, rely on tourism. When a tourist visits a new country or some unknown place to explore it he may be in difficulty due to no guidance. In some areas, you don’t know if an ATM is available on the mountain, if there is any washroom, or where you can camp. Is it safe to go in winter to northern areas, etc? Sometimes, more minor things can be risky for tourists. So a tourist needs proper guidance and planning such as hotel, money and security. A Tourism Guide System can meet their demands by providing personalized recommendations, interactive maps, and planning tools. To solve this problem you can develop a Tourist guide system as your final year project. Later, you can launch this system and earn money through AdSense or a paid subscription. Less is more! Source Code Recommended Reading Stock Price Prediction system

E Commerce Sales Forecasting The ecommerce market is growing, but it is essential to be ahead of competitors to succeed. Machine learning is one of the most innovative technologies that has changed the business world. This article explains how an ecommerce sales forecasting system can help businesses and give them a competitive edge. You will also learn how to develop an ecommerce sales forecasting system using a machine learning algorithm. I will also provide source code to help you build e-commerce sales predictions. However, you should know how this system works when developing an e-commerce sales forecasting system. Understanding the Basics of E-commerce Sales Forecasting An E-commerce sales forecasting system predicts future sales based on market trends, historical data, and many other factors. By traditional methods, it is very complex to predict sales because the ecommerce market is seasonal and tends to keep changing. Here comes machine learning, which will transform the business and help it grow. The Evolution of Machine Learning in E-commerce Machine learning and AI rapidly evolve, and machine learning algorithms have grown significantly. Machine learning provides various tools for ecommerce businesses to analyze vast datasets. ML algorithms have become more advanced, allowing accurate prediction and insights. Key Components of E-commerce Sales Forecasting Models E-commerce sales prediction models usually use a mix of past sales data, customer behaviour, seasonality, and outside factors such as economic trends. Machine learning algorithms analyse this data and find patterns that people might miss. This method makes estimates more accurate, which helps businesses better use their resources. Benefits of Using Machine Learning in E-commerce Sales Forecasting Improved Accuracy ML models can examine a lot of data and find small patterns and trends that traditional approaches might miss. This makes it easier to predict sales, so you don’t have to worry about having too much or too little stock. However, extensive data will help you in getting better accuracy in prediction. Real-time Insights ML algorithms can handle and analyze data in real-time, allowing businesses to know what’s happening. This skill is essential in the rapidly changing e-commerce environment, where market conditions can change quickly. Personalized Recommendations ML models can look at how customers act and what they like, which allows brands to make personalized product suggestions. This makes the experience better for the customer and makes it more likely that they will buy something. Implementing Machine Learning for E-commerce Sales Forecasting Data Collection and Preparation The first step is to gather valuable data. To make reasonable estimates, ML models need clean and well-organized data. It would help if you looked at past sales data, contacts with customers, and external factors. Choosing the Right Algorithm ML algorithms are flexible and can work with various data and business needs. To choose the best algorithm, it is better to know the advantages and disadvantages of each algorithm. Training the Model To learn patterns and trends, ML models need to be instructed with data from the past. The model can make more accurate predictions when the training data is more accurate. Continuous Monitoring and Updating The market changes all the time, and so does e-commerce. Regularly adding new data to the ML model, predictions remain accurate and valuable. Real-world Examples of ML in E-commerce Sales Forecasting Amazon’s Recommendation Engine: Amazon uses machine learning to look at how customers behave and what products they like, then makes personalized product suggestions. This not only makes the experience better for users, but it also dramatically increases sales.   Walmart’s Inventory Management: Walmart uses machine learning to handle its inventory, which adjusts stock levels based on past data and insights gained in real-time. Because of this, prices have gone down, and efficiency has gone up.   E Commerce Sales Forecasting using Machine Learning To make E commerce sales system. First of all download dataset from here. Data Loading and Libraries: Imports necessary libraries for data analysis and machine learning. Loads e-commerce data from a CSV file using Pandas. Data Cleaning and Preprocessing: Handles missing values, converts date columns, and deals with canceled invoices. Cleans and filters data based on stock codes, descriptions, and customer information. Exploratory Data Analysis (EDA): Explores and visualizes various aspects of the data, such as stock codes, descriptions, customer behaviors, and geographical distribution. Feature Engineering: Derives additional features related to daily product sales, revenue, and temporal patterns. Explores and preprocesses unit prices and quantities. Model Development: Utilizes the CatBoost regression model for predicting product sales. Defines hyperparameters and performs hyperparameter tuning using Bayesian Optimization. Time Series Cross-Validation: Implements time series cross-validation to evaluate model performance over different time periods. Multiple Model Management: Defines a class for managing multiple CatBoost models. Trains and evaluates models using various features and hyperparameters. Visualization and Interpretability: Utilizes SHAP (SHapley Additive exPlanations) for interpretability. Visualizes model results, hyperparameter optimization, and feature importance. E Commerce Sales Forecasting using Machine Learning with source code Pyton ## Warehouse optimization import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set() from catboost import CatBoostRegressor, Pool, cv from catboost import MetricVisualizer from sklearn.model_selection import TimeSeriesSplit from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans from scipy.stats import boxcox from os import listdir import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=UserWarning) warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=FutureWarning) import shap shap.initjs() data = pd.read_csv("../input/ecommerce-data/data.csv", encoding="ISO-8859-1", dtype={'CustomerID': str}) data.shape data[data.Description.isnull()].head() data[data.Description.isnull()].CustomerID.isnull().value_counts() data[data.Description.isnull()].UnitPrice.value_counts() data[data.CustomerID.isnull()].head() data.loc[data.CustomerID.isnull(), ["UnitPrice", "Quantity"]].describe() data.loc[data.Description.isnull()==False, "lowercase_descriptions"] = data.loc[ data.Description.isnull()==False,"Description" ].apply(lambda l: l.lower()) data.lowercase_descriptions.dropna().apply( lambda l: np.where("nan" in l, True, False) ).value_counts() data.lowercase_descriptions.dropna().apply( lambda l: np.where("" == l, True, False) ).value_counts() data.loc[data.lowercase_descriptions.isnull()==False, "lowercase_descriptions"] = data.loc[ data.lowercase_descriptions.isnull()==False, "lowercase_descriptions" ].apply(lambda l: np.where("nan" in l, None, l)) data = data.loc[(data.CustomerID.isnull()==False) & (data.lowercase_descriptions.isnull()==False)].copy() data.isnull().sum().sum() ### The Time period <a class="anchor" id="timeperiod"></a> data["InvoiceDate"] = pd.to_datetime(data.InvoiceDate, cache=True) data.InvoiceDate.max() – data.InvoiceDate.min() print("Datafile starts with timepoint {}".format(data.InvoiceDate.min())) print("Datafile ends with timepoint {}".format(data.InvoiceDate.max())) ### The invoice number <a class="anchor" id="invoiceno"></a> data.InvoiceNo.nunique() data["IsCancelled"]=np.where(data.InvoiceNo.apply(lambda l: l[0]=="C"), True, False) data.IsCancelled.value_counts() / data.shape[0] * 100 data.loc[data.IsCancelled==True].describe() data = data.loc[data.IsCancelled==False].copy() data = data.drop("IsCancelled", axis=1) ### Stockcodes <a class="anchor" id="stockcodes"></a> data.StockCode.nunique() stockcode_counts = data.StockCode.value_counts().sort_values(ascending=False) fig, ax = plt.subplots(2,1,figsize=(20,15)) sns.barplot(stockcode_counts.iloc[0:20].index, stockcode_counts.iloc[0:20].values, ax = ax[0], palette="Oranges_r")