Venkatesh Anantharamu's repositories
Computer-Vision-CNN-Image-Classification
You are provided with a training set and a test set of images of plant seedlings at various stages of grown. Each image has a filename that is its unique id. The dataset comprises 12 plant species. The goal of the competition is to create a classifier capable of determining a plant's species from a photo. The project is from a dataset from Kaggle. Link to the Kaggle project site: https://www.kaggle.com/c/plant-seedlings-classification/data
Ensemble-Techniques-Term-Deposit-Subscription-Prediction
Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio.
Feature-Selection-Model-Selection-and-Tuning
To predict the concrete strength using the data available in file "concrete.csv". Apply feature engineering and model tuning to obtain 85% to 95% accuracy.
Fundamentals-of-AI-ML
The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The data is widely used for collaborative filtering and other filtering solutions. However, we will be using this data to act as a means to demonstrate our skill in using Python to “play” with data.
Natural-Language-Processing-Twitter-US-Airline-Sentiment-Analysis
A sentiment analysis project based on the Twitter data that was scraped in February 2015. The data is only from US Airlines and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").
Neural-Networks-Bank-Churn-Prediction
Businesses like banks that provide service have to worry about the problem of 'Churn' i.e. customers leaving and joining another service provider. It is important to understand which aspects of the service influence a customer's decision in this regard. Management can concentrate efforts on the improvement of service, keeping in mind these priorities. Objective: Given a Bank customer, build a neural network-based classifier that can determine whether they will leave or not in the next 6 months.
Supervized-Learning-Personal-Bank-Loan-Modelling
This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.
Unsupervized-Learning-Credit-Card-Customer-Segmentation
AllLife Bank wants to focus on its credit card customer base in the next financial year. They have been advised by their marketing research team, that the penetration in the market can be improved. Based on this input, the Marketing team proposes to run personalised campaigns to target new customers as well as upsell to existing customers. Another insight from the market research was that the customers perceive the support services of the back poorly. Based on this, the Operations team wants to upgrade the service delivery model, to ensure that customers queries are resolved faster. Head of Marketing and Head of Delivery both decide to reach out to the Data Science team for help. Objective: To identify different segments in the existing customer based on their spending patterns as well as past interaction with the bank.