A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

Here I have created the analysis model of a Super market datasheet given by the company and have deployed the parameters successfully.

** Project done during the Data Science & Analytics Internship at The Sparks Foundation **

No description available

Data Science and Analytics Internship at The Sparks Foundation This repository contains all the tasks for the Data Science and Analytics Intern at The Sparks Foundation. TASK-1 Improve our LinkedIn profile. TASK-2 To Explore Supervised Machine Learning In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables. TASK-3 To Explore Unsupervised Machine Learning From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. TASK-4 To Explore Decision Tree Algorithm For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly. TASK-5 To explore Business Analytics Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore’. You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit. What all business problems you can derive by looking into the data? You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel)

Task-3 Exploratory Data Analysis - Retail

No description available

No description available

● Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’ ● As a business manager, try to find out the weak areas where you can work to make more profit. ● What all business problems you can derive by exploring the data?

No description available

This is a project I performed during my Intership @The Spark Foundation

No description available

Data Science And Business Analytics Internship GRIP The Spark Foundation GRIPNOV20

No description available

No description available

Task 3 :EXPLORATORY DATA ANALYSIS - RETAIL

Task 3 - Exploratory Data Analysis - Retail

Exploratory Data Analysis (Task #3) - Retail

Task#3 Exploratory Data Analysis Retail

Task 3 : Exploratory Data Analysis - Retail

Task #3 Exploratory Data Analysis- Retail

Task 3 : Exploratory Data Analysis-Retail

No description available

No description available

No description available

I performed 'Exploratory Data Analysis' on dataset 'SampleSuperStore'. Dataset link - https://bit.ly/3i4rbWl. Tool used - Python.

No description available

No description available

Dataset : SampleSuperstore

No description available