Feel free to visit FinViz website for fundamental ratios, technical indicators to news headlines and insider training data, it is a perfect stock screener. Furthermore, it has updated information on the performance of each sector, industry and any major stock index. Loaded the saved HTML files by identifying the folder path and directing BeautifulSoup to ‘read’ the table of headlines Parsed the scraped text into data and time, and Organized the data for Visualization and Analysis Implemented NLTK VADER for Sentiment Analysis, and Customized the Sentiment Scoring System Merged Sentiment scores and Headlines Data, and removed the duplicates to visualize the results Performed and Visualized the Sentiment on one single trading day for Facebook Stock Tesla: Facebook: Link to GitHub Repository

Loaded, Visualized, and summarized daily stock price data for ‘Amazon’, ‘Facebook’ and ‘S&P500’ Calculated daily stock returns data for ‘Amazon’, ‘Facebook’ and ‘S&P500’ Performed calculations for ‘Excess Returns: S&P 500 VS Stocks’ Computed ‘Mean’ and ‘Standard Deviations’ for Excess Returns Determined ‘Sharpe Ratio’ to help understand the return of an investment compared to its risk Link to GitHub Repository

Loaded and Viewed the confidential dataset, as the contributor of the dataset has anonymized the feature names Read this blog, to better understand the anonymized features Handled all missing values, as they affect the performance of machine learning model if they go unchanged Preprocessed the data into Three main tasks: Converted the non-numeric data into numeric, Splitting the data into train and test sets, and Scaled the feature values to a uniform range Fitted a Logistic Regression model (a generalized linear model), and Evaluated the model on the test set with respect to classification accuracy, and summarized the performance of a classification algorithm using Confusion matrix Performed GridSearchCV by defining the grid of values to two hyperparameters ‘tol’ and ‘max_iter’ to improve the model’s ability to predict credit card approvals Summarized the best achieved model score of 85% and the respective best parameters Link to GitHub Repository

Cleaned the data and conducted Exploratory Data Analysis Transformed hashable and comparable non-numerical labels to numerical labels using LabelEncoder Applied Polynomial and Linear Regression Machine Learning Models to predict the target variable ‘Insurance Cost’ accurately Link to GitHub Repository