Using Tweepy API to get tweets from Weratedogs twitter account and performing the data analysis full process from Data wrangling to cleaning and EDA to draw some insights

“Valuable insights comes from hard wrangling” Throughout this report we will review the back scene circumstances, steps, actions, encountered challenges and the result during doing the “Data Analysis Process” for “WeRateDogs” Twitter account.

Dive into the world of WeRateDogs with this Python-driven Twitter analysis project. Uncover insights from 2015 to August 1, 2017, exploring popular dog ratings, user engagement metrics, and evolving trends over time. Experience the charm of canine ratings through a concise and captivating data analysis journey.

Explore the journey of data wrangling and analysis in the WeRateDogs project. Using Python, gather, assess, and clean data from the Twitter archive of @dog_rates. Unveil insights through visualizations and uncover trends like decreasing retweets over time. For details, refer to wrangle_act.ipynb and wrangle_report.pdf.

In this project, we aim to perform some data analysis processes based on the collected data from the WeRateDogs Twittter account about dog breeds on the account with the most interesting rates and high or low reviews from the account participants on twitter as well as some interesting data that were well prepared from Udacity team about the project that are stored on Udacity servers that we need to download this data from Udacity servers programmatically to start our data analysis processes and data wrangling steps as well to achieve reach and useful insights from this data

"WeRateDogs" is a Twitter account that rates people's dogs with a humorous comment about the dog. the master_archive_data_df some facts about the dog lover interaction to the posts : 1-getting an overview about The most common dog_ classification by drow the relation between dog_ classifications and the number of tweets for each dog_ classification

Analysis of WeRateDogs tweets, including steps of gathering, assessing, cleaning up and producing insights.

This repo contains the insights and visualizations produced from the exploratory data analysis done on the WeRateDogs twitter data .

The project focuses on data wrangling, analysis, and visualization of the WeRateDogs Twitter account to gain insights into dog-related data.

Data analysis of the WeRateDogs Twitter archive as of August 1, 2017. Consisted of extracting, cleaning and analyzing the data and provide key insights.

This is a data analysis project that includes gathering, cleaning and analyzing tweets by WeRateDogs to reach helpful insights about dogs and people interactions.

In this analysis I went through the WeRateDogs Twitter account to gather insights about their audience, what type of post earns more engagement, and more.

Wrangle and analyze the WeRateDogs Twitter archive to uncover insights using Python. This project involves data gathering, assessment, cleaning, and exploratory data analysis (EDA), with results documented in a Jupyter Notebook.

Carried out the three steps of data analysis, *Gathering *Assessing, and *Cleaning process while also cleaning quality and tidiness issues in the WeRateDogs Dataset before producing excellent insights from the dataset.

Wrangle and analyze the WeRateDogs Twitter archive to uncover insights using Python. This project involves data gathering, assessment, cleaning, and exploratory data analysis (EDA), with results documented in a Jupyter Notebook.

This is a data analysis project involving the WeRateDogs (@dog_rates) twitter account. I had to gather 3 different data, assess them, clean them, and produce quality insights and visualizations out of them.

This repository contains the codes used to extract, wrangle and analyse the WeRateDog Dataset. It also contains the visualization and insights derived from the analysis.

#Twitter-@WeRateDogs This Project involves the data analysis of twitter channel @WeRateDogs. Responsibilites: Gathering the data using Twitter API with the help of python library Tweepy. Parsing the json results from API and store it in appropriate format. Assessing and Cleaning the data. Exploratory data Analysis Visualizing the data and reporting the observations/ Insights. Tool- Jupter Notebooks, Python(Numpy, pandas, tweepy, json, matplotlib etc)

Data analysis of the WeRateDogs Twitter dataset to uncover trends in tweet engagement, dog stage distribution, and breed popularity. Includes data cleaning, visualization, and insights revealing that “doggo” tweets drive higher engagement and Golden Retrievers are most common

In this project I used python and its data analysis libraries to analyse the tweet data for the WeRateDogs twitter account. I gathered the tweets archive for the WeRateDogs twitter account using Twitter's API. I then used data wrangling skills to assess and clean the data. I have found some interesting insights that I am going to share.

This is a data analysis project where twitter data for WeRateDogs account was wrangled, analyzed and summarized to conclude interesting insights and findings. It's a part of my Udacity Data Analyst Nanodegree projects, and it was successfully accepted from the first submission.

WeRateDogs is an account on twitter that rates people’s dogs, it forms part of Udacity's Project on Wrangling and Analyzing Data, where students are asked to gather data from multiple sources, clean the data, and to infer insights from their analysis.

A data wrangling project focused on assessing, cleaning, and merging datasets related to the WeRateDogs Twitter archive. The project demonstrates data gathering from various sources, programmatic and manual assessments, and quality and tidiness cleaning using Python (Pandas, NumPy). Final insights were saved and documented for analysis.

This project analyses the popular WeRateDogs Twitter dataset to clean, wrangle, and explore real-world social media data. Using Python, pandas, and other tools, the analysis focuses on assessing data quality, merging multiple sources, handling messy & unstructured content and uncovering insights about dog ratings, tweet popularity, and trends.

The dataset that I worked on is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog I imported the various tables needed for the project twitter_archived,image_predictions and tweet_json respectively. After wrangling the data,and did my analysis, I got some insights from the wrangled data.

This project is a real-world data gathered from twitter account 'WeRateDogs'. This data is analysed using Python and its libraries, and the data is gathered from a variety of sources and in a variety of formats. The analysis will focus on assessing its quality and tidiness, then clean it. After cleaning the data, some insights will be cleared with visualizations to be easy to understand.

The repository contains all the projects made in the Udacity DataScience NanoDegree. The projects are: A bikeshare analysis for double modal (bike + subway) trips in the cities of Chicago, Washington DC and New York. This projects lets the user decide if they want to check specific information about a city, month, etc. Average details like trip duration are provided. A Data Wrangling project for a DB containing information about the tweets made by the WeRateDogs account. More specifically, insights about popular breeds of dogs and their evolution through the years are presented. A project named "Investigate a Dataset" where we made an analysis of a dataset containing information about the patients and appointments made in one city of Brazil. More specifically, our objective was to find if alcoholism, handcap and scholarship status could of the patients could influence the end result of the appointment.

As part of the fulfillment of Udacity's Data analyst nano degree, i worked on a dataset from WeRateDogs Twitter Archive, a page that tweets humorous reviews of cute looking dogs. There was no particular objective in mind at the beginning of the analysis, i simply intented to flex my data wrangling skills, explore and see if there are any interesting insights to learn from the data, Thankfully there were quite a few! 1. Most of the tweets were made from an iphone. This is interesting, an indicator that the administrator of the page uses an iPhone. However there were a few times Twitter Web and Tweetdeck were used.  2. A lot of dog breeds were not specified, unfortunately. However i was very interested in how frequently the specified breeds occurred in the dataset. Golden retriever dogs were the most frequrntly rated breed of dog, however it is difficult to pick a clear winner here given the high number of unspecified breeds as seen below. I have also inserted a random picture of one the golden retrievers   3. The Soft coated wheaten terrier dogs had the highest ratings on average, Leap frogging uncategorized dog breeds. However, it is still not easy to pick a winner here until we can somehow get more data on the other dog breeds