Discover niharikabalachandra/Clustering-MiniProject Open Source

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Created over 7 years ago
Updated over 7 years ago

niharikabalachandra

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The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset. We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.

Stock-Market-Prediction-Using-Natural-Language-Processing

We used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.

Jupyter Notebook

Market-Risk-Management-with-Time-Series-Prediction-of-Stock-Market-Trends-

Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time series analysis and prediction of short-term tends in stock prices.

Jupyter Notebook

Gesture-Recognition-Using-RNN

This project investigates gesture recognition techniques using Leap Motion Controller to collect 3 dimensional hand features such as coordinates, position of the hand, etc. We then use this raw data to train a Long Short Term Memory Recurrent Neural Network that is able to recognize basic hand gestures

Python

PropensityToConvert

Propensity models make true predictions about a customer’s future behavior. With propensity models you can truly anticipate a customer's future behavior. Here we focus on building a combination of a Propensity to convert and a Propensity to buy models that can influence the kind of marketing campaigns we adopt and who we decide to target (predicted converters vs non-converters) leading to spend optimizations that eventually increase the ROI on digital marketing campaigns.

Jupyter Notebook