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anaconda-client

Anaconda Server Client

api_sb_7questionsapiremoved

SB API Mini-Project - 7 Questions

automatewithpython

If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple files Create, update, move, and rename files and folders Search the Web and download online content Update and format data in Excel spreadsheets of any size Split, merge, watermark, and encrypt PDFs Send reminder emails and text notifications Fill out online forms

c--users-1800r-desktop-aribnb2

capstone2_airbnb_pf

capstone_1

An initial and basic study of customer churn

clustering

K Means Clustering , DBSCAN. ALGOMERATIVE, Spectral clustering

curicculum-vitae

My Career Summary

customerchurnanalysis

Customer Churn Analysis

datacamp

DataCamp data-science courses

datachallenges

Data Challenges

facilitysql

This is the SQL Mini Project: The data you need is in the "country_club" database. This database contains 3 tables: i) the "Bookings" table, ii) the "Facilities" table, and iii) the "Members" table.

foodforecasting

https---github.com-carolynmassa-airbnb_sb_2

PRICE FORECAST

inferential_stats

jsonbworldbankproject

Find the 10 countries with the most projects. Find the top 10 major project themes (using column mjtheme_namecode). For some entries in the exercise above, the name is missing. Create a dataframe filling in the missing names. Import Packages, Load JSON file as a Dataframe, Inspect - Get a look at the state and structure of the dataset. Inspect the columns needed for the exercises. Find the 10 Countries with the Most Projects - Retrieve a value count of country names, which is sorted in descending order; save the first 10 results. Load JSON file as a String, Flatten, Inspect - The column needed to finish the exercises contains nested data, so it needs to be normalized. Find the Top 10 Major Project Themes - This is accomplished by a value count of project codes since names are known to be missing. Create a Dataframe Filling in the Missing Names - Sort the dataframe, fill empty cells with NULL values, then replace those by backfilling. Find the Top 10 Major Project Themes by Theme Name - Produce a more descriptive list of top projects by getting a value count of project names.

linearregression

Springboard Boston Housing Project

logisticregression

The Height and Weight question? Male or Female?

moviepredictionnaivebayes

seaborn-data

Data repository for seaborn examples

seattle-terry-stops

spark

Coding Exercise performed in DataBricks

supplychainpy

Supplychainpy is a Python library for supply chain analysis, modelling and simulation. The library assists a workflow that is reliant on Excel and VBA.

tradingapp

youtubetutorials

Repository for Youtube Tutorial Notebooks and Scripts