Moin Dalvi Email & Phone Number

Navi Mumbai, Mh, In

India

Bengaluru, Karnataka, India

In this project, we dive into building a Machine learning model for Resume Classification using Python and basic Natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc. A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of resumes that are received by the… Show more In this project, we dive into building a Machine learning model for Resume Classification using Python and basic Natural language processing techniques. We would be using Python's libraries to implement various NLP (natural language processing) techniques like tokenization, lemmatization, parts of speech tagging, etc. A resume classification technology needs to be implemented in order to make it easy for the companies to process the huge number of resumes that are received by the organizations. This technology converts an unstructured form of resume data into a structured data format. The resumes received are in the form of documents from which the data needs to be extracted first such that the text can be classified or predicted based on the requirements. A resume classification analyzes resume data and extracts the information into the machine-readable output. It helps automatically store, organize, and analyze the resume data to find out the candidate for the particular job position and requirements. This thus helps the organizations eliminate the error-prone and time-consuming process of going through thousands of resumes manually and aids in improving the recruiters’ efficiency. The basic data analysis process performed such as data collection, data cleaning, exploratory data analysis, data visualization, and model building. The dataset consists of two columns, namely, Role Applied and Resume, where the ‘role applied’ column is the domain field of the industry, and the ‘resume’ column consists of the text extracted from the resume document for each domain and industry. The aim of this project is achieved by performing the various data analysis methods and using the Machine Learning models and Natural Language Processing which will help in classifying the categories of the resume and building the Resume Classification Model. Show less

Bengaluru, Karnataka, India

There is wide consensus among scientists and policymakers that global warming as defined by the Intergovernmental Panel on Climate Change (IPCC) should be pegged at 1.5 Celsius above the pre-industrial level of warming in order to maintain environmental sustainability. The threats and risks of climate change have been evident in the form of various extreme climate events, such as tsunamis, glacier melting, rising sea levels, and heating up of the atmospheric temperature. Emissions of greenhouse… Show more There is wide consensus among scientists and policymakers that global warming as defined by the Intergovernmental Panel on Climate Change (IPCC) should be pegged at 1.5 Celsius above the pre-industrial level of warming in order to maintain environmental sustainability. The threats and risks of climate change have been evident in the form of various extreme climate events, such as tsunamis, glacier melting, rising sea levels, and heating up of the atmospheric temperature. Emissions of greenhouse gases, such as carbon dioxide (CO2) are the main cause of global warming.In this Project for Time Series Analysis and Forecasting Each step is taken like EDA, Feature Engineering, Model Building, Model Evaluation, Prediction table, and Deployment. Explaining which model to select on basis of metrics like RMSE, MAPE, and MAE value for each model. Finally explaining which model we will use for Forecasting.Better accuracy in short-term forecasting is required for intermediate planning for the target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating organization to make informed decisions. Exponential techniques, Linear statistical modeling, and Autoregressive models are used to forecast the emissions and the best model will be selected on this basis1.) Minimum error 2.) Low bias and low variance trade-off Show less