This assignment requires you to perform machine learning data analysis based on a bankruptcy dataset that’s free on Kaggle. Please use the dataset and perform machine learning analysis on this topic...

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This assignment requires you to perform machine learning data analysis based on a bankruptcy dataset that’s free on Kaggle. Please use the dataset and perform machine learning analysis on this topic and submit a research report. The report contains the following: · Abstract · Which classification algorithms will we use? · Logistic Regression · Naive Bayes Classifier · K-nearest neighbor · Decision Trees · Performance Measures? · Confusion Matrix · Precision · Recall/ Sensitivity · Specificity · F1-Score · AUC & ROC Curve · Motivation · Many businesses have gone into default due to the recent pandemic which may cause investors to be wary of providing further capital. We seek to provide a model that interprets whether a company will become bankrupt, which can be used by investors to provide confidence and guidance. In order to address this problem, we will use a company bankruptcy dataset found on Kaggle along with the help of classification algorithms like Logistic Regression and Support Vector Machine · Literature Survey · Whatever algorithms we choose we can find literature on · Methodology · Data research - Taiwan economy during 1999 to 2009. the great recession · Data Exploration and Visualization - look for correlation between features · Data Preprocessing - The data is very imbalanced. The total entries are 6,819 with 6,599 not bankrupt and 220 being bankrupt. This is an obstacle we would have to come up with a solution through random sampling (under sampling/oversampling). Feature engineering - removing/adding features to determine which yield the best results, experiment with attribute combinations. We have 95 features. · Data Cleaning - check for nulls, check for missing entries, etc. Handling text and categorical attributes, feature scaling · Data split into training and testing data. · Train and evaluate for different models · Seek better evaluation through fine tuning · Apply performance measures to find best algorithm or best feature combinations