anikch

Banking-Market-Analysis-using-Scala

Your client, a Portuguese banking institution, ran a marketing campaign to convince potential customers to invest in a bank term deposit scheme. The marketing campaigns were based on phone calls. Often, the same customer was contacted more than once through phone, in order to assess if they would want to subscribe to the bank term deposit or not. You have to perform the marketing analysis of the data generated by this campaign.

Bike-rental-prediction-based-on-env-season

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

Credit-EDA-Case-Study

This case study aims to identify patterns which indicate if a client has difficulty paying their instalments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

Google-Vision-Data-Extractor

This python code can be used to extract data from Google Vision output. After you process your file for OCR using Google Vision, the generated text extraction can be structured and attributes can be identified by using this code. Please check Read me for the details.

Hand-Gesture-Recognition-system

Imagine you are working as a data scientist at a home electronics company which manufactures state of the art smart televisions. You want to develop a cool feature in the smart-TV that can recognize five different gestures performed by the user which will help users control the TV without using a remote.

house-price-prediction-Ridge_Lasso

Housing price prediction model using Ridge and Lasso Regression.

Image-denoising-using-convolutional-autoencoder

The objective is to add some noise to the images and then use an Convolutional Autoencoder to denoise them.

Lead-Scoring-Case-Study

Identifying Hot Leads by generating Lead Score for all leads, so that leads having higher Lead Scores can be contacted with priority for achieving Higher Lead Conversion Rate.

RAG-LOTR-Long-Context-Reorder

Advanced RAG using RAG + LOTR + Remove Redundancy + Long Context Reorder

self_RAG

Self-RAG is a new framework to train an arbitrary LM to learn to retrieve, generate, and critique to enhance the factuality and quality of generations, without hurting the versatility of LLMs.

anikch/house-price-prediction-Ridge_Lasso

Housing price prediction model using Ridge and Lasso Regression.

anikch/Image-denoising-using-convolutional-autoencoder

The objective is to add some noise to the images and then use an Convolutional Autoencoder to denoise them.

anikch/Banking-Market-Analysis-using-Scala

Your client, a Portuguese banking institution, ran a marketing campaign to convince potential customers to invest in a bank term deposit scheme. The marketing campaigns were based on phone calls. Often, the same customer was contacted more than once through phone, in order to assess if they would want to subscribe to the bank term deposit or not. You have to perform the marketing analysis of the data generated by this campaign.

anikch/Bike-rental-prediction-based-on-env-season

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

anikch/Google-Vision-Data-Extractor

This python code can be used to extract data from Google Vision output. After you process your file for OCR using Google Vision, the generated text extraction can be structured and attributes can be identified by using this code. Please check Read me for the details.

anikch/Lead-Scoring-Case-Study

Identifying Hot Leads by generating Lead Score for all leads, so that leads having higher Lead Scores can be contacted with priority for achieving Higher Lead Conversion Rate.

anikch/PIMA-indian-diabetes-eda-prediction

Build a model to accurately predict whether the patients in the dataset have diabetes or not?

anikch/tensorflow-deep-learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

anikch/Ball-Tracking

anikch/Classifying-Reviews-of-Cars-and-Digital-Camera

Epinions.com is a website where people can post reviews of products and services. It covers a wide variety of topics. For this case study, we downloaded a set of 600 posts about digital cameras and cars and saved as “Eopinions.csv”. The dataset has 2 columns: ‘class’ and ‘text’. We need to predict 'class' based on 'text'.

anikch/Classifying_images_from_MNIST_Fashion_Dataset

The dataset is similar to MNIST but includes images of certain clothing and accessory. The objective is to classify images into specific classes using a single-layer perceptron & multilayer perceptron.

anikch/Clustering-of-BBC-News-Articles

Clustering BBC News articles using different types of vectorization, dimensionality reduction and clustering algorithms. Then giving appropriate names to the clusters.

anikch/Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

anikch/DNN-Example-with-TensorFlow-1.x

Building a deep neural network using TensorFlow 1.x for binary classification.

anikch/eda-comcast-consumer-complaints-analysis

This EDA has been performed on Comcast Consumer Complaints dataset.

anikch/NLP-basic-handson

Basic NLP Hands-on. Data Cleaning, Pre-processing, Tokenization, Vectorization (Tf-Idf, Count vectorizer, Presence/Absence vectorization etc.) using NLTK and sklearn library.

anikch/nlp-predict-tweets-about-real-disasters

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always clear whether a person’s words are actually announcing a disaster. In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified.

anikch/nltk-handson

Extracting, Cleaning and Pre-processing text data using NLTK

anikch/python-basics

Notebook contains basic python commands. It covers basic operations on different Python Data Structures, Comprehensions, Shallow copy/Deep Copy, Functions, Lambda Functions, Map-Reduce-Filter and some extra tips.

anikch/Telecom-churn-analysis-and-prediction

Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.

anikch/wiki-detox

See https://meta.wikimedia.org/wiki/Research:Modeling_Talk_Page_Abuse

anikch/Face-emotion-detection-using-CNN

anikch/HMM_POS_Tagging

HMM based POS tagging using Viterbi Algorithm