Project Overview

Netflix Data Analysis Using Tableau:

STEPS:

1.Data subset collected from online

2.Loading Libraries

3.Data Cleaning & Finding Missing values

4.Data Visualization

Technologies used:

• MySQL | SQL Server

. Python

• Tableau

• Statistics

DATA VISUALIZATION:

1. Select options for Type: Movies & Tv Shows

2. Vertical Bar Graph of Rating of Shows & Movies

3. Horizontal Bar Graph of Relation Between Top 10 Gerne and Movie & TV Shows count

4. Pie-chart for the Type: Movie and TV Shows

5. line Area Graph of Total Movies and TV shows by Year

6. Country wise Map of Total Movies and TV shows by Country

7. Showing Wordcloud For Country, Directors,Category, Gerne, Rating

This is a Power BI project that creates interactive dashboards for Netflix content visual analysis.

I use 2 datasets

• the first one is Netflix Movies and TV Shows on kaggle
• the second one is IMDb score from IMDb Datasets (note that we use title.basics.tsv and title.ratings.tsv) then we combine it all together using data_preparation.ipynb and the final preprocessed data is in data/netflix_titles_with_IMDB.csv

This project depicts 2 story points.

1. an overview of Netflix content
   

2. Top IMDB score contents
   

You can find it by following Power BI link below.

Covid-19-Data-Visualization-with-Tableau:

1. The objective of this project is to predict the price of avocado sold in the US using historical dataset.

2. Data represents weekly 2018 retail scan data for National retail volume (units) and price.

3. The data is as follows: Date - The date of the observation Average Price - the average price of a single avocado type - conventional or organic year - the year Region - the city or region of the observation Total Volume - Total number of avocados sold 4046 - Total number of avocados with PLU 4046 sold 4225 - Total number of avocados with PLU 4225 sold 4770 - Total number of avocados with PLU 4770 sold

   • PLU - Product Lookup codes

1. Prophet is open source software released by Facebook’s Core Data Science team.
2. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
3. Prophet works best with time series that have strong seasonal effects and several seasons of historical data.
4. For more information, please check this out: https://facebook.github.io/prophet/docs/quick_start.html#python-api