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1. Detecting Fake News with Python

Fake news can be dangerous. This is a type of yellow journalism and spreads fake information as news using social media and other online media. This is a common way to achieve a certain political agenda. Fake news may contain false and/or exaggerated claims. Social media algorithms often viralize these and create a filter bubble. In this, we will train on a news.csv dataset of shape 7796×4. Well mainly use two things- a TfidfVectorizer and a PassiveAggressiveClassifier.

2. Detecting Parkinsons Disease with XGBoost

Parkinsons disease is a progressive disorder of the central nervous system that affects over 1 million people in India every year. It affects movement and can be a cause of tremors and stiffness. This is a neurodegenerative disorder with 5 stages to it and affects dopamine-producing neurons in the brain. 

3. Color Detection with OpenCV and Pandas

As we all know that colors are made up of three primary colors: Red, Green, and Blue. Their intensities can be measured between 0 to 255 and by combining them we get 6 million different color values. The idea of this project is to get the name of the color from the color values. To implement this we use a dataset that has color values and labeled color names, then we calculate the shortest distance between each color and display the color name that has the shortest distance.

4. Speech Emotion Recognition with librosa

Speech Emotion Recognition (SER) is an attractive application of data science today as we constantly attempt to give the consumer a better experience. This includes recognizing human emotion and affective states from speech. Since voice often exposes underlying emotions with tone and pitch, it can be used to understand the users needs and use it to improve the service. 

5. Breast Cancer Classification with Keras

IDC (Invasive Ductal Carcinoma) is the most common form of breast cancer, forming about 80% of all breast cancer diagnoses. This is cancer that develops in milk ducts and then invades the fibrous/fatty breast tissue outside them. In this project, we use the IDC_regular dataset (with breast cancer histology images). Histology is the study of the microscopic structure of tissues. With 2,77,524 patches of size 50×50 from 162 whole mount slide images scanned at 40x, well learn to build a classifier to train on 80% of the dataset. Well use 10% of it for validation. Well be using Keras to define a CNN (CancerNet).