February 2024

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")

Stock market Price Prediction using machine learning In the world of finance, predicting stock prices has always been a challenging task. Investors are always seeking innovative tools to navigate the unpredictable stock market. Machine Learning (ML) is a ground-breaking technology that has brought about significant changes in various industries, including finance. In this article, we will explore a Stock market Price Prediction using machine learning. We will explain how it works, its impact and how can we make this project in Python. Source code is also available for Stock market Price Prediction. What is Stock Price Prediction system? A Stock Price Prediction System is like a smart tool that uses special machine learning to predict where the stock market is going. It looks at old stock data, figures out what details matter, and learns from them. This helps people who invest money in stocks to decide if they should buy or sell. The steps involve gathering and fixing old stock information, picking out the important parts, and choosing a computer method to learn from it all. After the computer learns, it can guess what might happen next in the stock market. But remember, even with these tools, the stock market is tricky and can surprise us. So, predictions aren’t always spot on. How Stock Price Prediction using machine learning works? Design a user friendly interface with elegant images. Add input fields where user can enter name of stock. when user enter stock your system should give output and predict the price of that stock.Accuracy is very important so accuracy of this system which I implemented is 86%.So for user prediction with accuracy matters.On the other hand there is machine learning at backend.First, historical stock data, including opening and closing prices, volume traded, and other financial indicators, is collected. This data is then cleaned and normalized to ensure accuracy. Relevant features influencing stock prices, such as trends and technical indicators, are selected. A suitable machine learning model, like linear regression or decision trees, is chosen. The model is trained on historical data, learning patterns and relationships. After training, the model is validated and tested on separate datasets to ensure its ability to make accurate predictions. Real-world Applications of Stock market Price Prediction using Machine Learning Algorithmic Trading Machine learning has greatly affected algorithmic trading. In algorithmic trading has pre-defined criteria where automated systems make trades. These algorithms analyze market conditions in real time, making quick decisions to maximize profits and reduce risks. Portfolio Management For managing portfolios investment firms are using machine learning. With the help of these systems improve portfolios by finding the best mix of assets, balancing risk and return for the best outcomes. Sentiment Analysis For predicting stock prices understanding market sentiment is very important. With the help of machine learning models analyze news articles, social media, and financial reports to determine public sentiment and provide insights on market trends. Challenges and Opportunities of Stock market Price Prediction using machine learning The Complexities in Prediction Although machine learning presents a lot of opportunities, there are still some challenges associated with it. For example, the market is constantly changing, and unpredictable events such as geopolitical disturbances or sudden economic shifts can disrupt traditional models. However, machine learning systems have the ability to continuously adapt by learning from new data and adjusting their predictions accordingly. Burstiness in Financial Markets Financial markets change quickly, with sudden big changes up or down. This is a big challenge for machine learning models. They need to be able to handle fast changes and make accurate predictions, even in turbulent conditions. Balancing Specificity and Context Finding the right balance between specificity and context is important. To accurately predict individual stock outcomes, it is crucial to have machine learning models that are simple to understand the specific characteristics of each stock. However, they also need to consider the overall market situation to avoid making limited decisions. Stock market Price Prediction using machine learning in Python Creating a stock market prediction using machine learning is simple. It is not difficult you just need to follow these steps. Before going to steps download Tesla Stocks Dataset here. Step 1: Import Libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import chart_studio.plotly as py import plotly.graph_objs as go from plotly.offline import plot from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import r2_score from sklearn.linear_model import LinearRegression import pandas as pd import numpy as np import matplotlib.pyplot as plt import chart_studio.plotly as py import plotly.graph_objs as go from plotly.offline import plot from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import r2_score from sklearn.linear_model import LinearRegression This step imports necessary libraries for data manipulation (Pandas and NumPy), visualization (Matplotlib, Plotly), machine learning model (Linear Regression), and evaluation metrics. Step 2: Read and Preprocess Data Tesla_data = pd.read_csv('tesla.csv') Head_data = Tesla_data.head() Tesla_data['Date'] = pd.to_datetime(Tesla_data['Date']) Tesla_data = pd.read_csv('tesla.csv') Head_data = Tesla_data.head() Tesla_data['Date'] = pd.to_datetime(Tesla_data['Date']) Here, it reads a CSV file (‘tesla.csv’) into a Pandas DataFrame, displays the first few rows, and converts the ‘Date’ column to a datetime format. Step 3: Display Information about the Data print(f'Dataframe contains stock prices between {Tesla_data.Date.min()} {Tesla_data.Date.max()}') print(f'Total days = {(Tesla_data.Date.max() – Tesla_data.Date.min()).days} days') print(f'Dataframe contains stock prices between {Tesla_data.Date.min()} {Tesla_data.Date.max()}') print(f'Total days = {(Tesla_data.Date.max() – Tesla_data.Date.min()).days} days') This prints information about the data, including the range of dates and the total number of days. Step 4: Box Plot for Stock Prices Tesla_data[['Open', 'High', 'Low', 'Close', 'Adj Close']].plot(kind='box') Tesla_data[['Open', 'High', 'Low', 'Close', 'Adj Close']].plot(kind='box') This creates a box plot to visualize the distribution of stock prices. Step 5: Set Layout for the Plot layout = go.Layout( title='Stock Prices of Tesla_data', xaxis=dict( title='Date', titlefont=dict( family='Courier New, monospace', size=18, color='#7f7f7f' ) ), yaxis=dict( title='Price', titlefont=dict( family='Courier New, monospace', size=18, color='#7f7f7f' ) ) ) layout = go.Layout( title='Stock Prices of Tesla_data', xaxis=dict( title='Date', titlefont=dict( family='Courier New, monospace', size=18, color='#7f7f7f' ) ), yaxis=dict( title='Price', titlefont=dict( family='Courier New, monospace', size=18, color='#7f7f7f'

Realtime Object Detection Welcome to the era where machines have grown to see and understand the world around us. Real time Object Detection using Machine Learning is revolutionizing how we interact with technology, making our lives more efficient and secure. In this article, we will explore the technology’s applications, algorithms, and how real time object detection impact on various industries object. At the end of the article, we will learn how to make real time object detection in Python. Source code can also help you make a real time object detection project. What is Real-Time Object Detection? Real time object detection is a computer vision technique in which system helps to detect and locate the object in a video or image in real time. So, the existing system of real time object detection take more time and lack of speed to process input data and identifying the object. Traditional method require numerous passes over an image or video to detect object but this system used YOLO advanced algorithm. YOLO is more faster and efficient than typical object detection algorithms. How Does It Work? The magic behind real-time object detection lies in machine learning and computer vision. Convolutional Neural Networks (CNNs) algorithm play a important role in breaking down an image into smaller parts and analyzing them layer by layer to recognize patterns and features. Applications of Realtime Object Detection Enhancing Security with Surveillance Systems In the realm of security, realtime object detection is a game-changer. From identifying intruders to detecting unusual behavior, this technology ensures swift response, reducing the risk of security breaches. Revolutionizing Autonomous Vehicles Autonomous vehicles heavily rely on real-time object detection to navigate through dynamic environments. From recognizing pedestrians to avoiding obstacles, this technology is the eyes and ears of self-driving cars. Retail’s Personal Shopping Assistant In the retail sector, real-time object detection is transforming the shopping experience. Smart mirrors can suggest outfits based on a customer’s choice, utilizing this technology to identify clothing items and accessories in real-time. Healthcare’s Diagnostic Support Within healthcare, real-time object detection aids in medical imaging, helping identify and diagnose conditions faster. From identifying tumors in X-rays to monitoring patient movements, the applications are vast. Key Algorithms in Real-Time Object Detection YOLO (You Only Look Once) Algorithm The YOLO algorithm is a pioneer in realtime object detection, dividing an image into a grid and predicting bounding boxes and class probabilities for each cell simultaneously. This leads to speedy and accurate results. Faster R-CNN (Region-based Convolutional Neural Network) Faster R-CNN introduced Region Proposal Networks (RPN) to generate potential bounding box proposals, enhancing accuracy in object detection. This algorithm strikes a balance between speed and precision. SSD (Single Shot Multibox Detector) SSD is renowned for its efficiency, performing object detection in a single forward pass through a neural network. It utilizes multiple scales to detect objects of various sizes, making it robust and adaptable. Realtime object Detection in Python First you need to install required libraries and caffe model.Download Caffe Model To make realtime object detection in python follow theses steps: Step 1: Importing Libraries # import the necessary packages from imutils.video import VideoStream from imutils.video import FPS import numpy as np import argparse import imutils import time import cv2 # import the necessary packages from imutils.video import VideoStream from imutils.video import FPS import numpy as np import argparse import imutils import time import cv2 This block imports necessary libraries for video processing and computer vision. VideoStream and FPS are from the imutils library. numpy is imported as np. argparse is used for command-line argument parsing. imutils, time, and cv2 are standard libraries for image processing. Step 2: Parsing Command-Line Arguments # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-p", "–prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "–model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "–confidence", type=float, default=0.2, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-p", "–prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "–model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "–confidence", type=float, default=0.2, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) This block uses argparse to handle command-line arguments. Three arguments are defined: -p or –prototxt: Path to Caffe ‘deploy’ prototxt file (required). -m or –model: Path to Caffe pre-trained model (required). -c or –confidence: Minimum probability to filter weak detections (default is 0.2). Step 3: Defining Classes and Colors CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) This block defines a list of class labels (CLASSES). Random RGB colors are generated for each class and stored in COLORS. Step 4: Loading Caffe Model # load our serialized model from disk print("[INFO] loading model…") net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) # load our serialized model from disk print("[INFO] loading model…") net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) The pre-trained Caffe model is loaded using cv2.dnn.readNetFromCaffe. The paths to the prototxt file and the model file are obtained from the command-line arguments. Step 5: Initializing Video Stream and FPS Counter # initialize the video stream, allow the camera sensor to warm up, # and initialize the FPS counter print("[INFO] starting video stream…") vs = VideoStream(src=0).start() time.sleep(2.0) fps = FPS().start() # initialize the video stream, allow the camera sensor to warm up, # and initialize the FPS counter print("[INFO] starting video stream…") vs = VideoStream(src=0).start() time.sleep(2.0) fps = FPS().start() Video stream is initialized using VideoStream with the default camera source (src=0). There is a 2-second delay (time.sleep(2.0)) to allow the camera sensor to warm up. FPS counter (fps) is initialized. Step 6: Main Loop for Video Processing # loop over the frames from the video stream while True: # grab the frame from the threaded video

AI Music Composer project with source code Think of a world where music making is done by  human musicians and computers that can think. The mix of computers and music has created something new and unique in the last few years. This article talks about the beautiful world of computer-made music and how computers learn from data and make music that we can enjoy. The Birth of AI Music Composition The journey of AI in music composition began with the goal of understanding and replicating the complexities of human creativity. Researchers and developers envisioned an AI system that could understand musical patterns, harmonies, and melodies, ultimately creating compositions that resonate with the emotional depth to those crafted by human composers. The Role of Machine Learning (ML) in Music Composition At the heart of AI music composition lies Machine Learning. ML algorithms, equipped with vast datasets of musical compositions, analyze patterns and structures to understand the sense of different genres and styles. This enables AI systems not only to replicate but also to innovate in music composition. Real-World Applications of AI Music Composer Classical Revival AI composers have shown an impressive ability to imitate the styles of classical masters like Bach and Beethoven. The revival of classical compositions with a modern twist showcases the versatility of AI in maintaining and developing musical traditions. Contemporary Creations AI composers are not limited to the past. They excel in creating modern music, and adapting to evolving genres and trends. From electronic beats to indie pop, AI-generated compositions are breaking new ground and resonating with diverse audiences. Challenges and Ethical Considerations Striking the Right Balance While AI music composition opens doors to endless possibilities, there’s a delicate balance to be maintained. Striking the right balance between innovation and keeping the authenticity of human creativity is a challenge that developers and musicians grapple with. Ethical Dilemmas in AI-Generated Music The question of authorship and ownership in AI-generated music raises ethical dilemmas. Who owns the rights to a piece composed by an AI? Exploring these ethical considerations becomes imperative as AI continues to play a more prominent role in the creative process. AI Music Composer project code To create an AI Music composer in python follow these steps Step 1: GPU Assignment and Mounting Google Drive # ASSIGNING GPU !nvidia-smi -L # MOUNTING GDRIVE from google.colab import drive drive.mount('/content/gdrive') # ASSIGNING GPU !nvidia-smi -L # MOUNTING GDRIVE from google.colab import drive drive.mount('/content/gdrive') Step 2: Installing Jukebox # INSTALLING JUKEBOX !pip install git+https://github.com/openai/jukebox.git # INSTALLING JUKEBOX !pip install git+https://github.com/openai/jukebox.git Step 3: Importing Libraries and Setting Up Distributed Environment # IMPORTING LIBRARIES import jukebox import torch as t import librosa import os from IPython.display import Audio from jukebox.make_models import make_vqvae, make_prior, MODELS, make_model from jukebox.hparams import Hyperparams, setup_hparams from jukebox.sample import sample_single_window, _sample, sample_partial_window, upsample, load_prompts from jukebox.utils.dist_utils import setup_dist_from_mpi from jukebox.utils.torch_utils import empty_cache # Setting up distributed environment rank, local_rank, device = setup_dist_from_mpi() # IMPORTING LIBRARIES import jukebox import torch as t import librosa import os from IPython.display import Audio from jukebox.make_models import make_vqvae, make_prior, MODELS, make_model from jukebox.hparams import Hyperparams, setup_hparams from jukebox.sample import sample_single_window, _sample, sample_partial_window, upsample, load_prompts from jukebox.utils.dist_utils import setup_dist_from_mpi from jukebox.utils.torch_utils import empty_cache # Setting up distributed environment rank, local_rank, device = setup_dist_from_mpi() Step 4: Model Configuration # Model configuration model = '5b_lyrics' hps = Hyperparams() # … (setting various hyperparameters) vqvae, *priors = MODELS[model] vqvae = make_vqvae(setup_hparams(vqvae, dict(sample_length = 1048576)), device) top_prior = make_prior(setup_hparams(priors[-1], dict()), vqvae, device) # Model configuration model = '5b_lyrics' hps = Hyperparams() # … (setting various hyperparameters) vqvae, *priors = MODELS[model] vqvae = make_vqvae(setup_hparams(vqvae, dict(sample_length = 1048576)), device) top_prior = make_prior(setup_hparams(priors[-1], dict()), vqvae, device) Step 5: Selecting Mode and Setting Up Sample Hyperparameters # Selecting Mode mode = 'primed' codes_file=None audio_file = '/content/gdrive/My Drive/primer.wav' prompt_length_in_seconds=12 # Setting up sample hyperparameters sample_hps = Hyperparams(dict(mode=mode, codes_file=codes_file, audio_file=audio_file, prompt_length_in_seconds=prompt_length_in_seconds)) # Selecting Mode mode = 'primed' codes_file=None audio_file = '/content/gdrive/My Drive/primer.wav' prompt_length_in_seconds=12 # Setting up sample hyperparameters sample_hps = Hyperparams(dict(mode=mode, codes_file=codes_file, audio_file=audio_file, prompt_length_in_seconds=prompt_length_in_seconds)) Step 6: Metas and Labels Setup # Metas and Labels setup metas = [dict(artist="Rick Astley", genre="Pop", total_length=hps.sample_length, offset=0, lyrics="YOUR SOUNG LYRICS HERE!")] * hps.n_samples labels = [None, None, top_prior.labeller.get_batch_labels(metas, 'cuda')] # Metas and Labels setup metas = [dict(artist="Rick Astley", genre="Pop", total_length=hps.sample_length, offset=0, lyrics="YOUR SOUNG LYRICS HERE!")] * hps.n_samples labels = [None, None, top_prior.labeller.get_batch_labels(metas, 'cuda')] Step 7: Sampling Configuration # Sampling Configuration sampling_temperature = .98 # … (setting up various sampling parameters) # Sampling Configuration sampling_temperature = .98 # … (setting up various sampling parameters) Step 8: Sampling Process # Sampling Process if sample_hps.mode == 'ancestral': # … (ancestral sampling) elif sample_hps.mode == 'upsample': # … (upsample handling) elif sample_hps.mode == 'primed': # … (primed sampling) else: raise ValueError(f'Unknown sample mode {sample_hps.mode}.') # Sampling Process if sample_hps.mode == 'ancestral': # … (ancestral sampling) elif sample_hps.mode == 'upsample': # … (upsample handling) elif sample_hps.mode == 'primed': # … (primed sampling) else: raise ValueError(f'Unknown sample mode {sample_hps.mode}.') Step 9: Listening to Audio and Memory Management # Listening to Audio Audio(f'{hps.name}/level_2/item_0.wav') # Memory Management if True: del top_prior empty_cache() top_prior=None upsamplers = [make_prior(setup_hparams(prior, dict()), vqvae, 'cpu') for prior in priors[:-1]] labels[:2] = [prior.labeller.get_batch_labels(metas, 'cuda') for prior in upsamplers] zs = upsample(zs, labels, sampling_kwargs, [*upsamplers, top_prior], hps) Audio(f'{hps.name}/level_0/item_0.wav') # Listening to Audio Audio(f'{hps.name}/level_2/item_0.wav') # Memory Management if True: del top_prior empty_cache() top_prior=None upsamplers = [make_prior(setup_hparams(prior, dict()), vqvae, 'cpu') for prior in priors[:-1]] labels[:2] = [prior.labeller.get_batch_labels(metas, 'cuda') for prior in upsamplers] zs = upsample(zs, labels, sampling_kwargs, [*upsamplers, top_prior], hps) Audio(f'{hps.name}/level_0/item_0.wav') AI music composers use technology and imagination to make new music. They work with machines that can learn from music and make their music. This makes music more fun and different, making us think about what it means and who makes it. We are constantly finding new ways to make music with AI, and we know that the best music comes from both humans and machines working together. For Further Exploration The Role of Machine Learning in Music Composition Ethical Considerations in AI-Generated Art and Music The Future of AI in Collaborative Music Creation

Credit Card Fraud detection using machine learning Many people use credit cards to buy things online or in stores. But sometimes, fraudsters steal credit card information and use it to buy stuff without permission. These credit card frauds can cause many problems for customers and companies. To stop credit card fraud, we need new ways to discover when someone uses a credit card that is not theirs. One of these new ways is machine learning. Machine learning is when computers learn from data and make predictions. This article will explain how machine learning can help us detect credit card fraud and make our money safer. You will also learn how to make Credit Card Fraud detection using machine learning. The Rising Wave of Credit Card Fraud Credit card fraud is a growing threat that affects millions of people globally. As technology continues to advance, fraudsters are also becoming more advance in their methods.From identity theft to skimming devices, criminals constantly develop new techniques to exploit vulnerabilities in the system. To tackle fraud effectively, a proactive and adaptive approach is necessary. The Role of Machine Learning in Financial Security Machine learning is a part of artificial intelligence, which is when computers do things that humans can do. Machine learning can help us stop credit card fraud. It can use smart rules to look at a lot of data, find what is normal and what is not, and catch fraudsters. This way, we can stop fraudsters before they do more harm. Credit Card Fraud detection using machine learning with source code To make credit card fraud detection using machine learning first you need to download dataset from here. Download Dataset Step 1: Import necessary libraries The code begins by importing required libraries, including Pandas for data manipulation, scikit-learn for machine learning, and specific modules like RandomForestClassifier, LogisticRegression, DecisionTreeClassifier, SVC, KNeighborsClassifier for different classification algorithms. import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score, confusion_matrix, classification_report import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score, confusion_matrix, classification_report Step 2: Load the dataset Reads a CSV file named ‘cdd.csv’ into a Pandas DataFrame. Assumes the dataset contains features and labels (‘Class’ column) where 1 represents fraud and 0 represents non-fraud. data = pd.read_csv('cdd.csv') data = pd.read_csv('cdd.csv') Step 3: Split the dataset Separates the dataset into features (X) and labels (y). Splits the data into training and testing sets using train_test_split from scikit-learn. X = data.drop('Class', axis=1) y = data['Class'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X = data.drop('Class', axis=1) y = data['Class'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Step 4: Standardize the features X = data.drop('Class', axis=1) y = data['Class'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X = data.drop('Class', axis=1) y = data['Class'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Step 5: Apply Random Forest Model random_forest_model = RandomForestClassifier(random_state=42) random_forest_model.fit(X_train, y_train) y_pred_rf = random_forest_model.predict(X_test) accuracy_rf = accuracy_score(y_test, y_pred_rf) conf_matrix_rf = confusion_matrix(y_test, y_pred_rf) classification_rep_rf = classification_report(y_test, y_pred_rf) print("Random Forest Model:") print(f"Accuracy: {accuracy_rf:.2f}") print(f"Confusion Matrix:n{conf_matrix_rf}") print(f"Classification Report:n{classification_rep_rf}") print("-" * 50) random_forest_model = RandomForestClassifier(random_state=42) random_forest_model.fit(X_train, y_train) y_pred_rf = random_forest_model.predict(X_test) accuracy_rf = accuracy_score(y_test, y_pred_rf) conf_matrix_rf = confusion_matrix(y_test, y_pred_rf) classification_rep_rf = classification_report(y_test, y_pred_rf) print("Random Forest Model:") print(f"Accuracy: {accuracy_rf:.2f}") print(f"Confusion Matrix:n{conf_matrix_rf}") print(f"Classification Report:n{classification_rep_rf}") print("-" * 50) Step 6: Apply Logistic Regression Model logistic_regression_model = LogisticRegression(random_state=42) logistic_regression_model.fit(X_train, y_train) y_pred_lr = logistic_regression_model.predict(X_test) accuracy_lr = accuracy_score(y_test, y_pred_lr) conf_matrix_lr = confusion_matrix(y_test, y_pred_lr) classification_rep_lr = classification_report(y_test, y_pred_lr) print("Logistic Regression Model:") print(f"Accuracy: {accuracy_lr:.2f}") print(f"Confusion Matrix:n{conf_matrix_lr}") print(f"Classification Report:n{classification_rep_lr}") print("-" * 50) logistic_regression_model = LogisticRegression(random_state=42) logistic_regression_model.fit(X_train, y_train) y_pred_lr = logistic_regression_model.predict(X_test) accuracy_lr = accuracy_score(y_test, y_pred_lr) conf_matrix_lr = confusion_matrix(y_test, y_pred_lr) classification_rep_lr = classification_report(y_test, y_pred_lr) print("Logistic Regression Model:") print(f"Accuracy: {accuracy_lr:.2f}") print(f"Confusion Matrix:n{conf_matrix_lr}") print(f"Classification Report:n{classification_rep_lr}") print("-" * 50) Step 7: Apply Support Vector Machine (SVM) Model svm_model = SVC(random_state=42) svm_model.fit(X_train, y_train) y_pred_svm = svm_model.predict(X_test) accuracy_svm = accuracy_score(y_test, y_pred_svm) conf_matrix_svm = confusion_matrix(y_test, y_pred_svm) classification_rep_svm = classification_report(y_test, y_pred_svm) print("Support Vector Machine (SVM) Model:") print(f"Accuracy: {accuracy_svm:.2f}") print(f"Confusion Matrix:n{conf_matrix_svm}") print(f"Classification Report:n{classification_rep_svm}") print("-" * 50) svm_model = SVC(random_state=42) svm_model.fit(X_train, y_train) y_pred_svm = svm_model.predict(X_test) accuracy_svm = accuracy_score(y_test, y_pred_svm) conf_matrix_svm = confusion_matrix(y_test, y_pred_svm) classification_rep_svm = classification_report(y_test, y_pred_svm) print("Support Vector Machine (SVM) Model:") print(f"Accuracy: {accuracy_svm:.2f}") print(f"Confusion Matrix:n{conf_matrix_svm}") print(f"Classification Report:n{classification_rep_svm}") print("-" * 50) Step 9: Apply K-Nearest Neighbors (KNN) Model knn_model = KNeighborsClassifier() knn_model.fit(X_train, y_train) y_pred_knn = knn_model.predict(X_test) accuracy_knn = accuracy_score(y_test, y_pred_knn) conf_matrix_knn = confusion_matrix(y_test, y_pred_knn) classification_rep_knn = classification_report(y_test, y_pred_knn) print("K-Nearest Neighbors (KNN) Model:") print(f"Accuracy: {accuracy_knn:.2f}") print(f"Confusion Matrix:n{conf_matrix_knn}") print(f"Classification Report:n{classification_rep_knn}") knn_model = KNeighborsClassifier() knn_model.fit(X_train, y_train) y_pred_knn = knn_model.predict(X_test) accuracy_knn = accuracy_score(y_test, y_pred_knn) conf_matrix_knn = confusion_matrix(y_test, y_pred_knn) classification_rep_knn = classification_report(y_test, y_pred_knn) print("K-Nearest Neighbors (KNN) Model:") print(f"Accuracy: {accuracy_knn:.2f}") print(f"Confusion Matrix:n{conf_matrix_knn}") print(f"Classification Report:n{classification_rep_knn}") Step 10: Taking input from the user # Taking input from the user print("Please enter values for the features to predict the class:") feature_names = X.columns.tolist() user_input = [] for feature in feature_names: value = float(input(f"Enter value for {feature}: ")) user_input.append(value) # Predicting using the trained model predicted_class = random_forest_model.predict([user_input]) print(f"The predicted class is: {predicted_class[0]} So it is Fraud …..") # Taking input from the user print("Please enter values for the features to predict the class:") feature_names = X.columns.tolist() user_input = [] for feature in feature_names: value = float(input(f"Enter value for {feature}: ")) user_input.append(value) # Predicting using the trained model predicted_class = random_forest_model.predict([user_input]) print(f"The predicted class is: {predicted_class[0]} So it is Fraud …..") Output To sum up, machine learning can help us stop credit card fraud. It is a big change in the way we fight against fraudsters who steal money. Machine learning can look at data, find what is normal and what is not, and catch bad people fast. It can also learn from new ways of stealing and stop them. As computers get better, working together with humans and machine learning will make our money safer. Learn more: Machine Learning for Credit Card

Hand Gesture Recognition in python As a final year project, you can develop a gesture recognition interface that will help you enable direct communication between humans and machines. Traditional methods, such as keyboards and mice, can limit communication between humans and machines. So, this project used machine learning techniques and infrared information to detect and classify hand gestures. The system consists of several stages: hand detection, gesture segmentation, feature extraction, gesture classification using five pre-trained convolutional neural network models and vision transformer, and human-machine interface design. To build this system, you can use several machine learning algorithms like support vector machine, K nearest neighbor, decision tree, and neural networks to recognize gestures. This article also provides source code to make this system easy to use. The Basics of Hand Gestures Let’s learn some simple things about hand gestures first. We use our hands to show many things by moving them, making different shapes, and putting them in different places. Machines can understand what we want to do by looking at these gestures. Applications of Hand Gesture Recognition There are many different and amazing things you can do with hand gestures recognition system. You can make your computer or smart home things do what you want by just moving your hand. Hand gestures can also help people get better after being sick or hurt, and make cars more fun and easy to use. Hand Gesture Recognition in python To make Hand Gesture Recognition in python follow these steps. Step 1: Install Required libraries Install following libraries by typing these commands in terminal pip install opencv-python pip install opencv-python pip install mediapipe pip install mediapipe Step 2: Import required libraries Create a new python file in VS Code and  start typing this code import cv2 import mediapipe as mp import cv2 import mediapipe as mp Step 3: Initialize MediaPipe Hands module mp_drawing = mp.solutions.drawing_utils mp_hands = mp.solutions.hands mp_drawing = mp.solutions.drawing_utils mp_hands = mp.solutions.hands Step 4: Set up webcam capture: cap = cv2.VideoCapture(0) cap = cv2.VideoCapture(0) Step 5: Create a loop for real-time processing with mp_hands.Hands( min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands: with mp_hands.Hands( min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands: Step 6: Inside the loop, read frames from the webcam success, image = cap.read() success, image = cap.read() Step 7: Convert and flip the image: image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) Step 8: Process the image using MediaPipe Hands results = hands.process(image) results = hands.process(image) Step 9: Draw hand landmarks on the image if results.multi_hand_landmarks: for hand_landmarks in results.multi_hand_landmarks: mp_drawing.draw_landmarks( image, hand_landmarks, mp_hands.HAND_CONNECTIONS) if results.multi_hand_landmarks: for hand_landmarks in results.multi_hand_landmarks: mp_drawing.draw_landmarks( image, hand_landmarks, mp_hands.HAND_CONNECTIONS) Step 10: Display the annotated image: cv2.imshow('MediaPipe Hands', image) cv2.imshow('MediaPipe Hands', image) Step 11: Break the loop if the ‘Esc’ key is pressed if cv2.waitKey(5) & 0xFF == 27: break if cv2.waitKey(5) & 0xFF == 27: break Step 12: Release the webcam and close the window when done cap.release() cap.release() Output To expand your knowledge, check out these links: OpenCV Documentation TensorFlow Tutorials Scikit-learn Official Documentation You did it! You learned a lot about hand gesture recognition in Python. You learned some simple things and also how to make it work in real time. You know a lot about this cool technology that can do amazing things. When you work on your own things, don’t forget that you can do anything you want. Can hand gesture recognition be used for virtual reality applications? Absolutely! Hand gesture recognition is widely used in virtual reality to enhance user interaction and create immersive experiences. Is it difficult to learn and implement hand gesture recognition in Python for a beginner? While it may seem complex at first, with the right resources and dedication, beginners can grasp the fundamentals and gradually master the techniques. How can hand gesture recognition benefit the healthcare industry? Hand gesture recognition is valuable in healthcare for applications such as rehabilitation exercises, where patients can interact with virtual environments to aid in their recovery. 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