Use Case:

1. 1.) Credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
2. 2.) The Objective is anonymized credit card transactions labeled as fraudulent or genuine

Solution:

• 1) The class datasets is type of unbalanced classification.
• 2) Thus, SMOTE is used for imbalanced labelled data.
• 3) Exploratory data analysis is carried out on feature input data.
• 4) Class data contains two classes fraudulent and not fraudulent.
• 5) Logistic regression with balanced class is used to predict fraudulent of credit card.

Use Case:

• 1) Company fetches the financial data from various clients as individual excel sheets. These sheets are then clubbed as zip file. From empirical model sheet, data should be fetched and processed.
• 2) The objective is to forecast sales of revenues from empirical sheet based on past sales.

Solution:

• 1) After extracting files,  data preprocessing is done. Stationarity of revenue is checked to describe future behavior.
• 2) fbprophet is applied on dataset to reach objective.

Use Case:

• Data contain samples of text. This text can contain one or more drug mentions. Each row contains a unique combination of the text and the drug mention.  The Objective is to predict the sentiment for texts contained in the test dataset for given the text and drug n

Solution:

• 1) Input Dataset is “text". The unstructured data is processed with raw data preprocessing followed by text preprocessing .
• 2) TFIDF featurization is used to convert preprocessed text into vectors.
• 3) Sentiment class data is imbalanced .
• 4) Thus, Sentiment Class data is performed over-sampling using SMOTE.
• 5) The misclassification error for each alpha value is plotted and best alpha value is used in Naive Bayes classifier .
• 6) The plotted confusion matrix is evaluated based on naive bayes classifier.