geekquad/Customer-Segments

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geekquad/Customer-Segments

geekquad

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Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

AlgoBook

A beginner-friendly project to help you in open-source contributions. Data Structures & Algorithms in various programming languages Please leave a star ⭐ to support this project! ✨

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Pixel-Processing

📷 This repository is focused on having various feature implementation of OpenCV in Python. The aim is to have a minimal implementation of all OpenCV features together, under one roof.

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quiz.ai

An Encrypted Automatic Multiple-Choice Question Generator for Self-Assessment Using Natural Language Processing

Color-Recognizer

An application that provides color names and HTML/RGB mappings as output.

Fraud-Detection

A Person Of Interest identifier based on ENRON CORPUS data.

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TEXTGEN

The main task of the character-level language model is to predict the next character given all previous characters in a sequence of data, i.e. generates text character by character.

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StandSum

A PyPI package that does extractive text summarizer using Cosine Methods in NLTK.

Deep-Dream

A computer vision program which uses a convolutional neural network to find and enhance patterns in images, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images.

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Lung-Cancer-Detection

Data Science Bowl Challenge (DSB3)

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Facial-Recognition-with-PCA

Face Recognition Implementation using PCA, eigenfaces, and SVM

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Feature-Scaling

A package that can transform features by scaling each feature into a given range. This is more lightweight and easy to use than sklearn.preprocessing.MinMaxScaler

Handwritten-Digit-MLP-Classification

Using Multi Layer Perceptron to build the model. Classifies the handwritten digits of the MNIST database with around 98% accuracy.

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binogauss

A package that can calculate Gaussian as well as Binomial distributions.

Titanic-Survival-Exploration

Very basic data exploration of the Titanic Dataset.

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Lasso-Ridge-Regression-and-Elastic_Net-Regularization-from-Scratch

Basic implementation of Lasso, Ridge Regression and Elastic-Net Regularization.

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Pencil-Sketch

Converts regular RGB images into Pencil-sketch.

Time-Series-Prediction-from-Scratch

Training a simple RNN to do time-series prediction. Given some set of input data, it will be able to generate a prediction for the next time step.

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Random-Forest-from-Scratch

A basic implementation of the Random Forest Classifier from Scratch and using Seaborn to find important features.

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Boston-Housing

Evaluating the performance and predictive power of a model. Cross questioned several concepts of ML for better understanding.

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Flower-Prediction

Basic Iris Flower Prediction. Learning how to host ML models using Flask and deploy it using Heroku.

CIFAR-10

Object Recognition in Images. This project uses CNN for the classification and recognition tasks.

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AdaBoost-from-Scratch

A basic implementation of AdaBoost algorithm from Scratch.

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Phyllotaxis

A just for fun project.

Text-Learning

Basic usage of NLTK. Implementation of concepts like Stemmer, TfIdf, and text.CountVectors

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Automatic-Essay-Scoring

A recurrent neural network method for determining the relationship between an essay and its grade. Using Long-Short Term Memory networks to represent the meaning of texts to demonstrate that a fully automated framework is able to achieve results.

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Classifying-Fashion-Clothes

Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28, gray-scale image, associated with a label from 10 classes.

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