Personal-projects

A culmination of all projects made in the field of machine learning, data science and NLP

• Abalone Age Prediction : The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem.

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• Breast Cancer Detection : Predict whether the cancer is benign or malignant using features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image such as radius, texture , perimeter, area etc.

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• Handwritten Digits Image Classification Using Neural Network : This Model uses Convolutional neural networks to recognize handwritten digits and convert them into digital image.

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• Heart Disease Detection : Various Machine Learning Algorithms such as SVM, Decision Tree, K Nearest Neighbour, Logistic Regression, Random Forest were used to detect heart diseases and their performance was evaluated on accuracy. Random Forest recorded highest accuracy of 90%

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• Image Classification using Convolutional neural network : Images were classified into various labels such as airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck using CNN. 72% accuracy was achieved

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• Insurance affordability prediction : Prediction for insurance affordability was done on factors such as age using neural networks. Accuracy achieved : 99.3%

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• Loan eligibility predicting: Prediction for loan eligibility was done on various factors such as age, gender and income using logisic regression. Accuracy achieved : 80%

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• OCR Extractor: Optical Character Recognition along with spacy is used here to retrieve information from resumes and convert them to text

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• Surge Prediction : Surge Pricing is a dynamic pricing method where prices are temporarily increased due to increased demand or limited supply. The model determines Surge in price based on source, destination and type of vehicle.

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• Tweet Analysis : Sentiment analysis of tweet on any topic by parsing tweet fetched from Twitter. Classify the tweet as negative or positive. Accuracy achieved : 94%

Solution