<commit_before>---
layout: post
title: UGRID Meeting 3 - Movies
feature-img: "img/texture3.png"
author: <NAME>
---

Hello everyone in this meeting we will start our analysis on cinematic entertainment, we would also like to do the following:

- *Wrap up the HR analytics dataset*: If you want to showcase your work please email us back.
- *Prepare for Datafest*: We want everyone to form teams and socialize. We'll try our best to answer any question about Datafest.
- *Schedule tentative plans*: Polling for mock datafest date, do we want Sunday classes for beginners, and the recruitment of officers.

---

#### Our main focus this week is to explore the Human Resource Analytics dataset:

IMDB 5000 Movie Dataset- <https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset>

If you need help getting started try answering these questions for your data analysis:

- How could you determine popularity for a movie?
- Which movie or actor would you recommend to a friend?
- If you just wanted to make money... what is your targeted audience or genre?
- Perform a sentiment or text analysis on the information given: Can you identify the difference between happiness(satisfcation) and excitement(hype)? Can you rank emotions and ratings? 
- Taken from kaggle, but will the number of human faces in movie poster correlate with the movie rating?

---

#### Optional Datasets:

1. The Academy Awards, 1927-2015: <https://www.kaggle.com/theacademy/academy-awards>

- Can you predict next academy award winner?
- Can you predict who will not win not win an award?
- Can you identify useful measures or metrics for determining how awards are presented?

2. Movie Dataset: <https://archive.ics.uci.edu/ml/datasets/Movie>

- What determines whether a movie gets a remake or not?
- Which actor, movie, or studio is trending? In other words, determine popularity.
- Can you identify TV tropes or recourring moments that keep appearing in cinematic histroy?

---

<commit_msg>Update 2017-02-25-UGRID_F17-Movies.md<commit_after>---
layout: post
title: UGRID Meeting 3 - Movies
feature-img: "img/texture3.png"
author: <NAME>
---

Hello everyone in this meeting we will start our analysis on cinematic entertainment, we would also like to do the following:
- *Wrap up the HR analytics dataset*: If you want to showcase your work please email us back.
- *Prepare for Datafest*: We want everyone to form teams, socialize, and answer questions about Datafest.
- *Schedule tentative plans*: Polling for mock datafest date, do we want Sunday classes for beginners, and the recruitment of officers.

---

#### Our main focus this week is to explore the Human Resource Analytics dataset:

(1) IMDB 5000 Movie Dataset- <https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset>

If you need help getting started try answering these questions for your data analysis:

- How could you determine popularity for a movie?
- Which movie or actor would you recommend to a friend?
- If you just wanted to make money... what is your targeted audience or genre?
- Perform a sentiment or text analysis on the information given: Can you identify the difference between happiness(satisfcation) and excitement(hype)? Can you rank emotions and ratings? 
- Taken from kaggle, but will the number of human faces in movie poster correlate with the movie rating?

---

#### Optional Datasets:

(2) The Academy Awards, 1927-2015: <https://www.kaggle.com/theacademy/academy-awards>

- Can you predict next academy award winner?
- Can you predict who will not win not win an award?
- Can you identify useful measures or metrics for determining how awards are presented?

(3) Movie Dataset: <https://archive.ics.uci.edu/ml/datasets/Movie>

- What determines whether a movie gets a remake or not?
- Which actor, movie, or studio is trending? In other words, determine popularity.
- Can you identify TV tropes or recourring moments that keep appearing in cinematic histroy?

---


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