marialin/finding-donors marialin/finding-donors

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created over 5 years ago
  • Updated over 5 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
marialin/finding-donors
github

marialin

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

finding-donors is a supervise learning project

More Repositories

1

capstone_stock_trading_policy_optimization

This capstone project optimizes stock trading policy using Yahoo Finance data and Generic Algorithm to improve ROI and evaluated Sharpe Ratio and Sortino Ratio. Results show that our proposed method outperforms the benchmark buy-and-hold strategy on average in terms of ROI, Sharpe Ratio and Sortino Ratio for both AAPL and SPY for both training data and testing data and back tested with four one-year data since 2014.
Jupyter Notebook
3
star
2

dog_project

Convolutional Neural Networks Image Classification (Tensorflow, Keras)
Jupyter Notebook
1
star
3

boston-housing

Predicting Boston Housing Prices
HTML
1
star
4

Teach_a_Quadcopter_to_Fly

Deep RL Quadcopter Controller: In this project, you will design an agent to fly a quadcopter, and then train it using a reinforcement learning algorithm.
1
star
5

titanic_survival_exploration

Introduction and Foundations Project: Titanic Survival Exploration In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive.
Jupyter Notebook
1
star
6

customer_segments

Project Overview In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes. Project Highlights This project is designed to give you a hands-on experience with unsupervised learning and work towards developing conclusions for a potential client on a real-world dataset. Many companies today collect vast amounts of data on customers and clientele, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company with future products and services that best satisfy the demands or needs of their customers. Things you will learn by completing this project: How to apply preprocessing techniques such as feature scaling and outlier detection. How to interpret data points that have been scaled, transformed, or reduced from PCA. How to analyze PCA dimensions and construct a new feature space. How to optimally cluster a set of data to find hidden patterns in a dataset. How to assess information given by cluster data and use it in a meaningful way.
Jupyter Notebook
1
star