Regression Analysis of Bigmart Sales Predictions
BigMart’s data scientists gathered sales data for 1559 goods from 10 locations in various cities in 2013.
In addition, certain characteristics of each product and retailer have been determined.
The goal is to create a prediction model and determine the sales of each product in each store.
BigMart will use this model to try to identify the qualities of items and shops that are important in growing sales.
Classification of Credit Card Fraud Detection
It’s critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren’t charged for things they didn’t buy.
The dataset covers credit card transactions done by European cardholders in September 2013.
This dataset contains 492 frauds out of 284,807 transactions that happened in the previous two days.
The dataset is extremely imbalanced; positive transactions account for 0.172 percent of all transactions.
It only has numerical input variables that have undergone a PCA transformation.
We are unable to give the original features and further background information about the data owing to confidentiality concerns.
The major components derived with PCA are features V1, V2,… V28; the only features not changed with PCA are ‘Time’ and ‘Amount.’
The seconds elapsed between each transaction and the first transaction in the dataset are stored in the feature ‘Time.’
The transaction Amount is represented by the feature ‘Amount,’ which may be utilised for example-dependent cost-sensitive learning.
The answer variable, ‘Class,’ takes the value 1 in the event of fraud and 0 otherwise.
Regression Analysis of Black Friday Sales
This dataset contains sales transactions from a retail location.
It’s a classic dataset for exploring and expanding your feature engineering abilities as well as day-to-day learning from a variety of purchasing situations.
This is a case of regression.
There are 550,069 rows and 12 columns in the dataset.
Classification of Breast Cancer Detection
A digitised picture of a fine needle aspirate (FNA) of a breast mass is used to compute features.
They define the features of the image’s cell nuclei.
OCR Image to Text Conversion and Extraction
The project used the pytesseract module to convert images to text, as well as regular expressions to extract specified fields from the retrieved text.
Classification of the Iris dataset
The data collection has three classes, each with 50 instances, each referring to a different species of iris plant.
One class is linearly separable from the other two; however, the subsequent two are not linearly separable.
Classification of Loan Prediction Analysis
All types of house loans are handled by Dream Housing Finance.
They operate in all urban, semi-urban, and rural settings.
Customers apply for a house loan first, and then the firm verifies their loan eligibility.
The company intends to automate the loan qualifying procedure (in real time) based on information supplied by customers while filling out an online application form.
Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and other facts are included.
To automate this procedure, they created a problem to identify the client groups that are qualified for a loan amount, allowing them to target these consumers individually.
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