Done what a QA needs to be done

Languages- Python|SQL|RFlask| Computer Vision-openCVExploratory Data Analysis-Missing Values, Outliers, Encoding TechniquesFeature Engineering|Feature Selection|Feature Extraction|Feature ScalingVisualization- Basics of Tableau, Seaborn, MatplotlibLibraries-Numpy, Pandas, Tensorflow, Keras, NLTK(Natural Language Tool Kit), SpacyPython Web scraping.Data Science| Statistics-Descriptive and InferentialMachine Learning: Supervised Learning Models-Regression models, Decision Tree, [Bagging, Boosting(Ensemble Models)] , KNN, SVM, NBC.Unsupervised Learning Models- K-Means Clustering, Hierarchical ClusteringDimensionality Reduction Techniques- PCA(Principal Component Analysis)| LDA(Linear Discriminant Analysis)Natural Language Processing(NLP)A chatbot (Basic Self Learning)Text Pre-ProcessingTechniques-Tokenization, Stemming, Lemmatization, Bag Of Words, Tf-Idf, and so on. Sentiment Analysis.Artificial Neural Networks| Perceptron||Multiple PerceptronCNN| LeNet and AlexNet |VGG16, VGG19|Google Net|ResNet|DenseNet,Transfer LearningRNN-Recurrent Neural Network| LSTM |GRU, AutoEncoders|GANSHyperParameter Optimization-GridSearchCV, Randomized SearchCVGradient Descent, SGD, Mini_batch SGD

Sales Analyst: All the Businesses data in the Uk was explored and was given what businesses to contact based on certain insights, trends, and patterns that help the company to sell Google my business platform.

I would look at the customer's Prescriptions(Medical Data) and segregate them based on different categories they belong to and would derive patterns out of it.