Data Science Portfolio

Repository containing portfolio of data science projects completed by me for self learning, internship and hobby purposes. Presented in the form of iPython Notebooks.

Instructions for Running Python Notebooks Locally

1. Install dependencies using requirements.txt.
2. Run notebooks as usual by using a jupyter notebook server, Vscode etc.

Contents

• Machine Learning (ML)

  • IRIS Data Classification: A model to predict a flower class based on its specific features using various statistical analysis tools. The dataset contains three classes of flowers, Versicolor, Setosa, Virginica, and each class contains 4 features, ‘Sepal length’, ‘Sepal width’, ‘Petal length’, ‘Petal width’.

  • Red Wine Quality Prediction: A model to predict the quality of Red wine that will help producers, distributors, and businesses in the red wine industry better assess their production, distribution, and pricing strategy.

  • Loan Price Prediction: A model to predict wether a customer is eligible for loan amount. It’s a classification problem, given information about the application we have to predict whether they’ll be able to pay the loan back or not.

  • Google Stock Price Prediction: Google Stock Price Prediction using machine learning to discover the future value of company stock and other financial assets traded on an exchange to gain significant profits.

  • Flight Price Prediction: We have been provided with prices of flight tickets for various airlines and between various cities, using which we aim to build a model which predicts the prices of the flights using various input features.

  • Breast Cancer Prediction: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. The goal is to classify whether the breast cancer is benign or malignant.

  • Bank Note Authentication: The aim of this model is to predict whether a given banknote is authentic given a number of measures taken from a photograph.

  • Black Friday Sales Prediction: The aim of the model is to determine the product prices based on the historical retail store sales data to earn more profits.

  • Banglore House Price Prediction: A model that predicts Bangalore house rate to help people to know about the prices of house in various places without the need of contacting different agents for the same.

  • Zomato Predictive Analytics: Zomato Predictive Analytics explores a sneak peek into the Zomato restaurants data and which might help answer a few important questions.

  • Adult Census Income Prediction: The Goal is to predict whether a person has an income of more than 50K a year or not. This is basically a binary classification problem where a person is classified into the >50K group or <=50K group.

• Deep Learning (DL)

  • Butterfly Species Classification: A deep learning model to predict butterfly species by giving an image as an input.

  • Customer Churn Prediction: A deep learning model to predict which customers are likely to leave a service or to cancel a subscription to a service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones.

  • Fake News Classification: The objective is to classify the news from the dataset as fake or true and also to detect what kinds of topics or keywords appear frequently in real news versus fake news. I have used Bi-Directional Long Short Term Memory to classify news as fake or true news.

  • Face Recognition: Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time. Law enforcement may also use mobile devices to identify people during police stops.

  • Face Emotion Recognition: Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger, fear, surprise, sadness, happiness and so on.

  • Object Detection: Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

  • Hotel Customer Prediction: An ANN project that predicts the customers who are going to check-In in a hotel based on their past records. The project can help hotels to keep track of their customers and improve their services as to avoid cancellation. Booking cancellations have a substantial impact in demand management decisions in the hospitality industry.

• Natural Language Processing (NLP)

  • Twitter Sentiment Analysis by collecting own data: A NLP model trained on self collected twitter hashtag data which classifies the sentiments of the sentences entered by the user.

  • Twitter Sentiment Analysis: A NLP model trained on twitter data which classifies the sentiments of the sentences entered by the user.

  • Spam Classifier: A NLP model to classifiy sentences or emails as SPAM or HAM.

  • Movies Recommendation System: A NLP model which recommends moviees to user based on Cosine Similarity.

• Exploratory Data Analysis (EDA)

  • Superstore EDA: A deep Exploratory Data Analysis of US Superstore to Explore Buisness Analytics to find out the weak areas where we can work to make more profit and deriving the business problems by exploring the data.

Tools: scikit-learn, Pandas, Seaborn, Matplotlib, Numpy, Tensorflow, Flask, NLTK, Keras, OpenCV

If you liked what you saw, want to have a chat with me about the portfolio, shoot an email at [email protected]