Team member: Tianliang Tao, Steve Zhu

Github Repo: https://github.com/TianliangTao/Project.git

Goal

In this project, our goal is that studios and investors can use forecasting methods to predict the revenue that a new movie can generate based on some given information, like budget, runtime, popularity, and so on.

In this blog, I will create a simple webapp using Flask.

1.Enable Submissions

Create a submit template. This template inlude a text box for message, a text box for name, and a submit button.

In this blog, I will develop and assess a fake news classifer using Tensorflow.

In this assignment, I will use several new skills and concepts related to image classification in Tensorflow. And use Tensorflow to to distinguish between pictures of dogs and pictures of cats.

§1. Load Packages and Obtain Data

First setp is loading the package that we might use in this project.

import os
import tensorflow as tf
from tensorflow.keras import utils, models, layers
import matplotlib.pyplot as plt
import numpy as np

Then, access the data that contains labeled images of cats and dogs. ```python

location of data

_URL = ‘https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip’

In this blog, I will discover my favorite movies on the IMDB website. I will use webscraping to find the movie or TV shows that share actors with my favorite movie or shows.

Assignment:Write a tutorial explaining how to construct an interesting data visualization of the Palmer Penguins data set.

In this blog, I will use the NOAA climate data to create several in interactive data graphics.

1.Create a Database

In this blog post, you’ll create several interesting, interactive data graphics using the NOAA climate data that we’ve explored in the first several weeks of lectures.

Fortunately, it’s pretty easy to embed interactive HTML figures produced via Plotly on your blog. Just use plotly.io.write_html() to save your figure. Then, copy the resulting HTML file to the _includes directory of your blog. Finally, place the code

In this blog post assignment, you’ll create a short post for your new website. The primary purpose is to give you some practice working with Jekyll blogging with Python code.

In this post, I’ll show how to create a helpful histogram of some synthetic data.

In this post, we’ll see some examples of how to create technical posts that include Python code, explanatory text, and notes about your learnings. We’ll go over the primary methods that you’ll use to embed content in your posts.

In this post, we’ll get set up with Jekyll. Jekyll is a static site converter, which you can use to turn plaintext documents into attractive webpages.

The purpose of this exercise is to get you set up with the software tools we’ll use in PIC16B, including Anaconda, git + GitHub, and Jekyll.