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Mit Patel's Projects

airbnb-high-booking-rate-prediction icon airbnb-high-booking-rate-prediction

Airbnb, Inc. is an american vacation rental online marketplace company based in San Francisco, California, United States. Airbnb offers arrangement for lodging, primarily homestays, or tourism experiences. The company does not own any of the real estate listings, nor does it host events; it acts as a broker, receiving commissions from each booking. In this project we are trying to predict whether a listing would receive high booking or not.

analysis-for-kickstarter icon analysis-for-kickstarter

Kickstarter (https://www.kickstarter.com) has established itself as the leading platform for funding creative ventures. Aspiring entrepreneurs in the arts can initiate fundraising campaigns on Kickstarter to support their projects. Some projects have been hugely successful, whereas many others have fallen well short of their fundraising objectives. The attached data file contains sample data on over 4000 Kickstarter fundraising campaigns. Each row contains a summary of each campaign, including the goal and amount pledged, the state of the project in securing funding (e.g., successful, failed), the category of the project (i.e., type of art), and whether the project was featured via a staff pick or spotlight (i.e., on the Kickstarter home page).

analysis-of-health-inspections-across-prince-george-s-county icon analysis-of-health-inspections-across-prince-george-s-county

The Prince George’s County Health Department provides several services to residents through the Food Protection/Policy Program, including: routine inspections for food service facilities such as restaurants, grocery stores, catering facilities, theater and stadium concessions, public and private schools, penal institutions, shelters and senior feeding programs; inspections for temporary or seasonal food operations including vending locations, carnivals, fairs, festivals and farmers markets; and inspections for mobile food units. The program responds to inquiries concerning any licensed or unlicensed food service facilities or operations.

analysis-of-regional-traffic-count-of-national-capital-region icon analysis-of-regional-traffic-count-of-national-capital-region

Metropolitan Washington Council of Governments has organized and published annual average daily traffic by vehicle classifications for the National Capital Region. The suggested dataset for this Challenge represents the average hourly traffic counts between 6 and 9 A.M. in a typical 2017 weekday. As the regional transportation planning organization, we are interested in knowing what can be told about the traffic conditions of the morning rush hours in the Capital Region. We used ARCGIS to create map visualization and finding traffic patterns.

aspect-based-opinion-mining-nlp-with-python- icon aspect-based-opinion-mining-nlp-with-python-

Aspect-Based Opinion Mining involves extracting aspects or features of an entity and figuring out opinions about those aspects. It's a method of text classification that has evolved from sentiment analysis and named entity extraction (NER). ABOM is thus a combination of aspect extraction and opinion mining. While opinions about entities are useful, opinions about aspects of those entities are more granular and insightful.

comprehensive-h1-b-visa-data-analysis-using-python icon comprehensive-h1-b-visa-data-analysis-using-python

H-1B is a visa category in the United States of America under the INA, section 101(a)(15)(H) which allows U.S. employers to employ foreign workers. The first step employer must take to hire a foreign worker is to file the Labor Condition Application. In this project, we will analyze the data from the Labor Condition Application.

employee-performance-prediction-using-r-shiny-and-random-forest-classifier icon employee-performance-prediction-using-r-shiny-and-random-forest-classifier

Human resource has become one of the main concerns of managers in almost all types of businesses which include private companies, educational institutions and governmental organizations. Business Organizations are really interested to settle plans for correctly selecting proper employees. After hiring employees, managements become concerned about the performance of these employees where management build evaluation systems in an attempt to preserve the good performers of employees. Using data mining techniques we are trying to predict an employee's performance band based on their historical evaluation and performance reviews.

home-credit-default-risk-detection icon home-credit-default-risk-detection

Home Credit risk has been a prevalent concern in the housing sector for decades. Credit risk is defined as “the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations.” This risk is especially important in the housing and financial sector where a default can result in a financial institution not being paid back. That can result in the financial institution not being able to meet their own obligations or carry out their own operations. In addition to financial loss, defaults can also result in the financial institution not extending credit to other borrowers, which can have a broader impact on the economy.

lifeblood-1 icon lifeblood-1

A Web-Based Application to Connect Blood Donators and Blood Seekers

rossmann-stores-uk-sales-prediction- icon rossmann-stores-uk-sales-prediction-

Our project scope is to apply machine learning techniques to a real-world problem of predicting store sales. Germany’s largest store chain, has provided past sales information of 1115 stores located across Germany. We pre-processed, feature engineered the data, and examined 2 different machine learning algorithm for forecasting sales of store: Random Forest regression, and XGBoost. Then, we compared the method’s predictive power by computing Root Mean Square Percentage Error (RMSPE). We found that XGBoost model performed the best with a RMSPE score of 0.11 validation data set. Deployment has been done with Flask Web-App.

talent-onboarding-using-r-shiny icon talent-onboarding-using-r-shiny

Through the process of onboarding, we help new talent find their place in the company, both in terms of job responsibilities as well as within organizational culture. During this process, the new hire is likely to become more and more emotionally and intellectually invested in not only their position but also in the company as a whole. During this process, companies can use this tool to find the right set of projects for the new hires based on a competency match between the candidate skills and the ongoing project competencies.

talent-pipeline-using-r-shiny icon talent-pipeline-using-r-shiny

A talent pipeline is defined as a ready pool of potential candidates who are qualified and prepared to step up and fill relevant key roles within the organization as soon as they fall vacant. This on-hold talent pool can include internal employees who show promise and can be promoted from within the organization as well as candidates from external sources like referrals, online job portals, career web-pages. A pipeline of both active and passive candidates helps in perceptive and proactive workforce planning. With a ready pool of right talent, the cost and time to hire can be reduced considerably. Organizations today, in spite of operating in a largely candidate-led market, do not have the luxury to wait for candidates to take the lead and apply. They need to have prospective candidates prepared – the machinery all wound up and ready to be set into motion - before the need arises for them to fill in a role.

video-game-sales-data-analysis icon video-game-sales-data-analysis

The data set used in this analysis describes information about historical video game sales. Categorical information includes: rank (in sales), title, platform (system), year of release, genre, publisher, and sales amount (in millions) for the regions of: North America, Europe, Japan, and Other Countries. Approach to Data Analysis includes using Pandas, reading and using Python library documentation, and incorporating data visualization with analysis with Series and DataFrames.

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