Apart from standalone projects, I will highly recommend you to work on open-source projects to get even more exposure to industrial practices and tools. It helps you understand the project lifecycle and prepares you for professional life. Project link: Automatic Speech Recognition using Facebook wav2vec2-xls-r-300m Furthermore, you will learn to clean audio and text data and use n-gram language models to improve the WER performance metric. After that, you will use HuggingFace transformers to build and improve multi-language speech recognition models. In this project, you will learn to handle and process audio and text data. It took me two months to understand everything about handling audio data and processing it to create an automatic speech recognition model. This is a bit of an advanced project for anyone. Project link: An End-to-End Project on Time Series Analysis and Forecasting with Python The project goes deep into time-series analysis, and I will highly recommend this project to all final-year students. After that, you will train and evaluate the ARIMA model and use the predictions to compare past and future trends. In this project, you will analyze the data and then visualize the trend to come up with a better strategy for forecasting. Companies are developing ways to understand the patterns and trends to avoid disasters and earn more profits for stakeholders. There is a huge demand for time series analysis and forecasting in the financial market. Time Series Analysis and Forecasting End-to-End Project Project link: Flight Price Prediction with Flask appĤ. You will understand how to handle the data and deploy your machine-learning solution. If you are a beginner, this is the perfect start you want. In this project, you will clean the data, perform exploratory data analysis, visualize the data to understand the trend of ticket prices, train and evaluate the model, and build model inference using Flask. Project link: Instagram Reach Analysis using Pythonģ. In this project, you will analyze the Instagram dataset, use various visualization graphs to explain the patterns and trends, and finally create a simple machine-learning model to predict the reach of an Instagram post. The data scientist has to clean the data, perform statistical analysis, add data visualization charts, explain the visualization to stakeholders in non-technical language, and perform predictive analysis. It is about understanding the data and explaining it in layman's language. Project link: How to Scrape Stock Prices from Yahoo Finance with PythonĪnalytical projects are not about creating fancy visualization. You need to understand various Python tools to create scraping scripts or web spiders for a constant stream of live data from various websites. Web Scraping is the most essential part of data analysts, BI engineers, and data scientist's jobs.
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