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Ndidi Anyakora's Projects

ab-test-analysis-new-menu-launch icon ab-test-analysis-new-menu-launch

Projects Submitted for Udacity's Predictive Analytics for Business Nanodegree using Alteryx and Tableau. This project involves using A/B test to write up a recommendation to whether a coffee shop/chain should launch a new menu. As a Business analyst, you are expected to analyze the results of the experiment conducted by the market research team and figure out whether the new menu can drive enough sales to offset the cost of marketing the new menu. Also determine whether the menu changes should be applied to all stores. The predicted impact to profitability should be enough to justify the increased marketing budget: at least 18% increase in profit growth compared to the comparative period while compared to the control stores; otherwise known as incremental lift.

asana-s-early-career-data-science-take-home-assessment icon asana-s-early-career-data-science-take-home-assessment

The project analyzed Asana user data to determine adoption rate and factors influencing adoption. After data cleaning, an adoption rate of 12% was calculated. Predictor variables were extracted and modeled using Random Forest and Decision Tree classifiers. Both models performed well, with Random Forest achieving 87% accuracy.

capital-one-data-science-challenge icon capital-one-data-science-challenge

Utilizing historical transaction data from specific accounts, the objective is to create a supervised machine learning model capable of predicting future transactions and effectively distinguishing between valid and fraudulent ones.

combining-predictive-techniques icon combining-predictive-techniques

Projects Submitted for Udacity's Predictive Analytics for Business Nanodegree using Alteryx and Tableau. This capstone project has three main tasks. Task 1: Store Format for Existing Stores: to provide analytical support to make decisions about store formats and inventory planning. Task 2: Determine the Store Format for New Stores: Develop a model that predicts which segment a store falls into based on the demographic and socioeconomic characteristics of the population that resides in the area around each new store. Task 3: Forecasting Produce Sales: prepare a monthly forecast for produce sales for the full year of 2016 for both existing and new stores.

data-science-nigeria-capstone-project icon data-science-nigeria-capstone-project

Data Science Nigeria Insurance claims - Wednesday Capstone Project. This project involves using machine learning skills to build a predictive model that can provide the total amount of claim for a customer in seconds using available features, thereby creating a fair and unbiased system. This was based on solving the complaints from both the staffs of a highly reputable Johnson Insurance Company and its customers. Many customers allege that it takes quite a long time before their claim is approved, and some were not satisfied with the amount they got. Hence the need for fast regulatory compliance.

meme-generator icon meme-generator

Project Submitted for Udacity's Intermediate Python Nanodegree using Python and Flask. This project is a simple meme generator (a multimedia application to dynamically generate memes, including an image with an overlaid quote) written in python. This project was created as part of Udacity's Python Nanaodgree program. It includes a Flask based web interface and a cli interface. This project will have the the following attributes: - Interact with a variety of complex filetypes. - Load quotes from a variety of filetypes (PDF, Word Documents, CSVs, Text files). - Load, manipulate, and save images. - Accept dynamic user input through a command-line tool and a web service. Packages used: Project uses `python 3`, `random`, `PIL`, `abc`, `argparse`, `typing`, `pandas`, `docx`, `os`, `subprocess`, `requests`, `flask`.

near-earth-objects-udacity icon near-earth-objects-udacity

Project Submitted for Udacity's Intermediate Python Nanodegree using Python. This project involves using a dataset obtained from NASA’s Center for Near-Earth Object Studies (CNEOS) close approaches of NEOs to Earth. A close approach occurs when an NEO's orbit path brings it near Earth - although, "near" in astronomical terms can be quite far in human-scale units, such as kilometers. The project should inspect the properties of the near-Earth objects in the data set and query the data set of close approaches to Earth using any combination of the following filters: Occurs on a given date, on or after a given start date, on or before a given end date, approaches Earth at a distance of at least (or at most) X astrononical units, approaches Earth at a relative velocity of at least (or at most) Y kilometers per second, has a diameter that is at least as large as (or at least as small as) Z kilometers and is marked by NASA as potentially hazardous (or not).

predict-diamond-prices icon predict-diamond-prices

A jewelry company wants to put in a bid to purchase a large set of diamonds, but is unsure how much it should bid. In this project, a predictive model will be used to make a recommendation on how much the jewelry company should bid for the diamonds.

satellite-television-call-centre-request icon satellite-television-call-centre-request

The dataset attached is of calls made to a Satellite Television call centre and contains records of different types, ranging from complaints to enquiries. You are expected to create visualization(s) based on the provided dataset with which the company Chief Operations Officer will use to make key decisions.

worldquant-university-data-science-projects icon worldquant-university-data-science-projects

I have successfully completed a 16-week and 8 end-to-end, applied data science projects of the Applied Data Science Lab module at WorldQuant University. The mini-projects included scientific computing, data wrangling, machine learning and natural language processing with Python.

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