In order to solve the actual vehicle routing problem, an intelligent order allocation algorithm for booking trips online between cities is proposed. The algorithm uses the time and space information of the order to construct the initial order allocation scheme set, then uses local search to optimize the order allocation scheme, and dynamically allocates the new orders according to the appointment time, and finally provides different order allocation schemes for decision makers. By constructing the order allocation scheme and optimizing the order allocation scheme, the algorithm can obtain high-quality solutions in a short time.

FlyAway (An Airline Booking Portal). Project 2 DESCRIPTION Project objective: As a Full Stack Developer, design and develop an airline booking portal named as FlyAway. Use the GitHub repository to manage the project artifacts. Background of the problem statement: FlyAway is a ticket-booking portal that lets people book flights on their website. The website needs to have the following features: ● A search form in the homepage to allow entry of travel details, like the date of travel, source, destination, and the number of persons. ● Based on the travel details entered, it will show the available flights with their ticket prices. ● Once a person selects a flight to book, they will be taken to a register page where they must fill in their personal details. In the next page, they are shown the flight details of the flight that they are booking, and the payment is done via a dummy payment gateway. On completion of the payment, they are shown a confirmation page with the details of the booking. For the above features to work, there will be an admin backend with the following features: ● An admin login page where the admin can change the password after login, if he wishes ● A master list of places for source and destination ● A master list of airlines ● A list of flights where each flight has a source, destination, airline, and ticket price The goal of the company is to deliver a high-end quality product as early as possible. The flow and features of the application: ● Plan more than two sprints to complete the application ● Document the flow of the application and prepare a flow chart ● List the core concepts and algorithms being used to complete this application ● Implement the appropriate concepts, such as exceptions, collections, and sorting techniques for source code optimization and increased performance You must use the following: ● Eclipse/IntelliJ: An IDE to code for the application ● Java: A programming language to develop the web pages, databases, and others ● SQL: To create tables for admin, airlines, and other specifics ● Maven: To create a web-enabled Maven project ● Git: To connect and push files from the local system to GitHub ● GitHub: To store the application code and track its versions ● Scrum: An efficient agile framework to deliver the product incrementally ● Search and Sort techniques: Data structures used for the project ● Specification document: Any open-source document or Google Docs The following requirements should be met: ● The source code should be pushed to your GitHub repository. You need to document the steps and write the algorithms in it. ● The submission of your GitHub repository link is mandatory. In order to track your task, you need to share the link of the repository. You can add a section in your document. ● Document the step-by-step process starting from sprint planning to the product release. ● The application should not close, exit, or throw an exception if the user specifies an invalid input. ● You need to submit the final specification document which will include: ● Project and developer details ● Sprints planned and the tasks achieved in them ● Algorithms and flowcharts of the application ● Core concepts used in the project ● Links to the GitHub repository to verify the project completion

Chatbots in Tourism Hospitality Industry: The future of chatbot is here; this technology has recently witnessed rapid diffusion in many sectors. Basic versions of chatbots are currently utilized, which usually start conversations with easy automated options for patrons and offer basic services like ordering or booking. However, fully functional chatbots that will be ready to replace customer service personnel will likely become more widespread by 2020, with AI bots powering 85% of all customer service interactions. Chatbots have the potential to assist the tourism industry in many ways – Chatbots in Tourism Hospitality Industry For any industry, accessibility to the company’s offerings is vital to the customer in both the pre-sale and therefore the post-sale process. Now, as more and more people are using instant messaging services like Facebook Messenger and WhatsApp, this simple use is often further enhanced by a company’s offering all of its services where consumers are afore chatting with their friends. Performing common administrative and menial tasks through chatbots, like scheduling appointments, setting reminders, booking tickets, and sharing traffic or weather updates, is very valued. Although there are some potential pitfalls, discussed later, the potential of chatbots in diverse sectors of the tourism industry is gigantic. Hotels, restaurants, hire car services, travel agencies, and tourist information centers can all enjoy this technology. The hotel industry can particularly enjoy the direct application of chatbots. Increasing the share of online bookings impacts sales growth, confirming the value of the hotel chatbot. Expedia took advantage of Facebook’s technology to launch a basic bot to assist travelers book hotels. Marriott Hotels also introduced a chatbot service to supply basic services like booking an area over chat, utilizing the Facebook chatbot interface. Chatbots are often particularly helpful in enriching the prearrival experience, allowing users to book rooms and other amenities, like: Spa Treatments Airport transfers Dinner Reservations Chatbots in the Hotel Industry A bot that interacts with guests in the least stages of the customer journey can gather valuable data, which algorithms and hotel staff alike can then use to supply personalized services. The direct application of chatbots within the restaurant business is often very impactful also. Restaurants and nutriment giants like Burger King, Pizza Hut, and Dominos have followed suit with their proprietary chatbots. Soon placing delivery orders over the phone is going to be obsolete; customers will do that through Facebook, WhatsApp, or other social networking sites. Chatbots will eventually accept payments as well; MasterCard already provides such services through its Masterpass app. Chatbots in the Restaurants Positioning chatbots can decrease costs for both customers and firms. Customers don’t get to call, which reduces their communication expenditures, and corporations will not get to hire customer service representatives or outsource answering services to a call center facility. The advantages aren’t limited to the ordering and delivery processes. Other possible chatbot benefits highlights include allowing customers to perform subsequent tasks without having to download mobile apps: Observe and survey restaurant reviews, menus, prices, and available tables Control restaurant reservations on the go, change, cancel, or re-book tables Search and find restaurants consistent with party size, date, time, preferred cuisine, price, or distance. Chatbots in the Airline Industry – Chatbots in Tourism Hospitality Industry Customer service within the airline industry is one of the primary areas that would enjoy chatbots as a result of the high volume of customer contact through inquiries and bookings. an honest customer service bot could economize by automating tasks and unclogging call centers. It might help consumers find suitable flight options by meeting information like time, date, and other preferences. It could help on the wing booking, saving customers the difficulty of visiting the airline’s website and entering page after page of data. It could give status updates about flights, like information about delays or cancellations. It could also provide digital boarding passes, a service Turkish Airlines has begun to provide; offer baggage information; and gather feedback. it’s reported that its introduction has recorded an enormous surge in online booking. Chatbot Challenges Although AI and chatbots have created excitement within the tourism and hospitality industry, many concerns and problems can affect their adoption. The media’s portrayal of AI as being capable of handling much of the tasks within the tourism and hospitality industry is sometimes overrated. the push toward chatbots is partly thanks to the recognition of several new messaging services. The testing with chatbot adoption involves technical issues, cost, culture, and organization size. one among the foremost significant technical issues in language processing. Chatbots still commonly struggle with lexical and semantic ambiguity. We have study the role of chatbots in several areas of the tourism and hospitality industry. This is often the age of chatbots. As an information-intensive industry, firms that lead in its early adoption are set to experience first-mover advantage, that is, the benefit gained by being the primary to launch a service. The interlinked nature of the tourism industry will subject industry laggards into undue pressures, which can not be favorable to their strategic directions at that point. So, the time to plan is now!

Problem Statement This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same. Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen. Downloads: You can download the dataset file from the link given below: Bike Sharing Dataset Download Assignment - Data Dictionary Download Submissions Expected: Python Notebook: One Python notebook with the whole linear model, predictions, and evaluation. Subjective Questions PDF: Apart from the Python notebook, you also need to answer some subjective questions related to linear regression which can be downloaded from the file below. Answer these questions and submit it as a PDF. Note: There are some questions in the subjective questions doc that you might not be familiar with. So you're expected to research these questions and give an appropriate answer in order to expand your learnings of this topic.

This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same. Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen.

Problem Statement In this Problem, you have to write an application in Python that keeps track of traffic fines. All violations are noted by a traffic policeman in a file as a record <license number of driver, fine amount>. At the end of each day, files from all traffic policemen are collated. If a driver had been charged with more than three violations so far, then he has to be booked for further legal action. Also, the police department provides additional bonus to those policemen who have brought in large fine earnings. All policemen who have collected equal to or more than 90% of the highest total fine collected by an individual policeman, shall be awarded the bonus. The program should help the police department answer the below queries: 1. Find out the drivers who are booked for legal action: All such license numbers are to be output in a file called “violators.txt”. 2. Find out the policemen who are eligible for bonus: The list of policemen eligible for bonus must be output in a file called “bonus.txt”. Additionally, 3. Perform an analysis of questions 1 and 2 and give the running time in terms of input size, n. Use hash tables for keeping track of drivers (and their violations), and a binary tree for keeping track of policemen (and their bookings). Data structures to be used: DriverHash: A separately chained hash table indexed by license numbers where each entry is of the form < license number, number of violations>. A simple hash function h(x) = x mod M, where M is the size of hash table can be used for this. Functions: def initializeHash (self): This function creates an empty hash table that points to null. 2. def insertHash (driverhash, lic): This function inserts the licence number lic to the hash table. If a driver’s license number is already present, only the number of violations need to be updated else a new entry has to be created. 3. def printViolators (driverhash): This function prints the serious violators by looking through all hash table entries and printing the license numbers of the drivers who have more than 3 violations onto the file violators.txt. The output should be in the format --------------Violators------------- , no of violations 4. def destroyHash (driverhash): This function destroys all the entries inside the hash table. This is a clean-up code. 5. def insertByPoliceId (policeRoot, policeId, amount): This function inserts an entry <policeId, amount> into the police tree ordered by police id. If the Police id is already found in the tree, then this function adds up to the existing amount to get the total amount collected by him. This function returns the updated tree. 6. def reorderByFineAmount (policeRoot): This function reorders the Binary Tree on the basis of total fine amount, instead of police id. This function removes the nodes from the original PoliceTree, and puts it in a new tree ordered by fine amount. Note that if the fine amount in node i is equal to the amount in node j, then the node i will be inserted to the left of the node j. This function returns the root node of the new tree. 7. def printBonusPolicemen (policeRoot): This function prints the list of police ids which have earned equal to or more than 90% of maximum total fine amount collected by an individual. The output is pushed to a file called bonus.txt. The output will be in the format -------------- Bonus ------------- , no of violations def destroyPoliceTree (policeRoot): This function is a clean-up function that destroys all the nodes in the police tree. 9. def printPoliceTree (policeRoot): This function is meant for debugging purposes. This function prints the contents of the PoliceTree in-order. Sample file formats Sample Input file Every row of the input file should contain the / / in the same sequence. Save the input file as inputPS3.txt

Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set.

## Deep Lea Project Description Booking cancellations in hospitality industry have risen due to Online Travel Agencies (OTA) making it as the main selling point in their marketing campaign. The increase makes it harder for hotels to accurately forecast, leading to non-optimized occupancy and revenue lost. This causes direct financial consequences as well as operational problems. In order to solve this problem, we will use a real-life hotel booking dataset to create a customer segmentation analysis in order to gain insights about the customers (and hopefully reasons why they cancel their reservation). We will then build a classification model (including the newly created customer clusters) to predict whether or not a booking will be canceled with the highest accuracy possible. #### Dataset Description Our goal is to build a model able to classify a booking as canceled or not canceled. The dataset provides data from real bookings scheduled to arrive between July, 1st 2015 and August, 31st 2017 from two hotels in Portugal (a resort in the Algarve region (H1) and a hotel in the city of Lisbon (H2)). Booking data from both hotels share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. For a detailed list and description of those variables refer to the data dictionary.

This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same. Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable.

Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen.

Booking Ordering Problem

From the Smove challenge

A core order service that solves the problem of booking a resource for a period of time.

A Super app that can solve the problem of juggling between multiple apps to do basic daily life tasks like, ordering food, chatting, booking rides, managing fiat and crypto wallet and much more. The whole application is powered by AI voice assistant.

This software or web application is intended for implementing “The Healing Infirmary” system that would make the life of patients much easier. All the patients don’t need to go through various kind of hassle for booking appointment with doctors by going to hospital. Patients can easily access from anywhere by sit at home and they can register online to book an appointment with the doctor at ease. They can also order any kind of medicine through online using doctor’s prescription and can book for ambulance service. Patients can interact online and consult their problems with the doctor using the system. Patients can also book for emergency ambulance booking system without logging into the system. There will be payment system that will be much easier for the patients as any kind of payment will be done through online mobile banking system and credit card system.

There are several packages available for a travel system on the web In order to select the best package to certain destination , there is no efficient recommendation system available. To overcome this problem , we are coming up with Travel Package. Recommendations System, where you can select the best package. In this project, there are two types of users, one is for the admin and another is for the User. An admin logs into his account , and his role is to add , edit , and delete packages , manage tour pakages, users, bookings , issues , enquiries . And an provide recommendations accordingly. A user login logs in and provides the personalized ( tourist package and events ) to the system and the best package that is available on web whih will be presented to the user.

Food fairy aims to bridge the gap the individuals who want to donate food and the organizations looking for food in order to help the people who really need them. We want food donation to be hassle free so that each one of us can donate to our full extent easily. Now all you need is a good heart to donate food which we know ypu already have because all the other things will be taken care by us. The problem was clearly evident that most of the food we waste is totally edible and is wasted because we dont have any other easy option. The solution to this problem is also that simple. If you have a bunch of food with you which is totally edible, visit our website and make a contribution. Please enter your contact details and the type of food you want to donate. After that enter your location, as soon as you enter your location you can see all the active organisations around you, their volunteers can come and get your food really soon. then please confirm booking after that you can track the volunteer which will come and get your food. YAY you just made a contributi It was never so simple to bring happiness in the life of others Right!!!!. If you want to make a contribution , please visit FOOD FAIRY now

This app is all about saving your time . We have created an application to know the live status of traffic @ foodcourt of SAP Bangalore location. The main aim is to let people make informed decision at a particular time in choosing a foodcourt. This will save your time as you will know in advance which food court has the availability for you and your friends. The app will have the option to let you know which food is available in which food court if suppose you are interested in one main course special dish. The App gives you the option of booking a table if there is a Team Lunch. We are providing a weekly graphical analysis which will predict the best time to eat at a particular foodcourt based on historic data. Considering the impact of food wastage, we are also extending the app for saving food. We have integrated a live feedback status of food items so that you may choose not to take a particular food item if for example the feedback says ” it is spicy” hence saving food wastage. For the demo purpose we will concentrate on the Food Courts but in future implementation of this application we are planning to extend it to other common places of SAP location like Gym, Ordering Food from private outlets at SAP location. This app provides a solution to a very basic problem of availability in a gamified approach to provide an all in all complete time saving application

We all know in which moving houses is usually a stress filled interval inside our life which enable it to get the far better folks. But smart taking can certainly spend less you the particular trial and therefore it's important to start off taking at the least six several weeks before you call up the actual removalists that may help you move. How to make?: Help to make a directory of items to possibly be stuffed; this can be completed bedroom by simply area. Using this method you should understand those items that could be forgotten or maybe given away. If you have a closet, require a share of things as well as it will have a great deal things held with storage space that you'll n't need inside fresh property. Through losing unneeded items you will put away within the supplying containers. Commence with taking the items which are certainly not at this time used, one example is in the event that it is summer you are able to pack your complete winter weather clothing ahead of. This is furthermore pertinent for you to added crockery, cutlery and many others. Make contact with any Removalist: It really is easier to talk with a removalist locally and find a new estimate before hand. A fantastic removalist bureau will certainly information and provide an individual shifting points. Arranging upfront will assist you to prepare much better and maybe also assert an easily affordable offer through the removalist. You can even get tough providing containers from the removalist in which can save you the problem regarding amassing cardboard bins. A specialist removalist may also help you dismantling in addition to reassembling home furniture and attending to all of your valuables. Merely make certain you obvious the most questions just before booking a new removalist. Coordinating: Reveal this manager attributes with a person as soon as packaging. Label each of the bins using their articles listing with top and also aspect. Don't forget to place guidance just like 'fragile' or 'kitchen articles', this will assist the movers organize these types of packing containers although packing your pick up truck. You may also variety the particular packing containers which is critical should some sort of package will go lost even though relocating. Generally depart some area in order to seal the particular boxes properly. Utilize plenty of bubble encapsulate as well as paper to help group vulnerable weapons like glassware as well as crockery. Cover every glassware or even crockery individually then heap these individuals inside a sturdy packaging container. Once you have dismantled home furniture, group all the products, anchoring screws for example inside hoagie hand bags. Maintain an outside hoagie tote for every single various furniture content. Wrap the pieces of furniture using robust underlay items to protect them by chafes along with bruises. Almost all removalists may help you using home furniture extra padding.For more information click on these links- http://top8th.in/packers-movers-bangalore/ http://www.list4pm.in/packers-movers-bangalore/ http://www.list4pm.in/packers-movers-pune/

Since the beginning of time, man has been very innovative .He used to find a solution to every problem in a unique way .There were times when people had no clothes to wear and they used leaves to cover their body ,but with the evolution of time, these leaves were replaced by cloth .In winters that cloth was not enough to protect the man from the extreme cold so he figured out a simple way to protect himself from cold .He used the animal skin to keep warm.in the begining he used the animal skin mostly in its raw form without cutting it according to one’s size and body shape ,but as society became civilised this raw protection was transformed into beautiful genuine leather jackets. Now ,the use of leather jackets have become very popular ,especially among youth it is considered a thing to treasure and no man’s wardrobe is complete without a leather jacket .Today’s advancement in technology has made buying a genuine leather jacket online a very simple and quick task to do .The internet is now full of information about different websites that sell leather jackets at most affordable rates in different designs and styles .The more you search the internet the more resources you will find that assist you in buying genuine leather jackets for men. Nature made objects look a class apart than man- made things but today there are many factors that try to misguide the customers by using ambiguous terms in their description about the products. There are a lot of similar looking things being sold in the market. Buying genuine leather jacket online is not an easy task .One has to be very careful and be able to differentiate leather from synthetic as a genuine leather jacket is quite an expensive product. Buying men’s leather jacket online is a risky business because in online shopping one does not have a chance to touch and feel the product one has to buy. There are many items that need to be touched to know the quality of a product and buying a product without touching like believes in what you see or hear. When you plan on buying a genuine leather jacket for men online ,search the internet till you find an online store that has a real store too and guarantees the originality of the product .It is best if you find a website with free shipping and money back guarantee for the non-satisfied customers. If you are able to find a store that books orders online and is situated in your locality or just at a distance where you could reach easily try to visit the store before booking an order. Many times one buys such things online to send it as a gift to someone and provides the address of the person for whom we want to send the gift just to surprise the receiver, so, especially in such cases we should check with the stores and select the jacket we like and note down its product brand name and product identification number.