if you are planning on going out to see a movie,how well ca you trust online reviews and ratings?Especially if the same company showing rating also makes money by selling movie ticktes. Do they have a bias towards rating movies higher than they should be rated

Your goal is to complete the task below based off the 538 article and see if you reach a similar conclusion.you will need to use your pandas and visualisation skills to determine if Fandango's ratings in 2015 had a bias rating movies better to sell more tickets

Task: Read this artcile : Be Suspicious Of Online Movie Ratings, Especially Fandango’s

Task: After Reading the article , read these two tables giving an overview of the two .csv files we will be working with:

The Data

This is the data behind the story Be Suspicious Of Online Movie Ratings, Especially Fandango’s.There are two csv files one with Fandango Stars and Displayed Ratings , and the other with aggregate data for movie ratings from other sites , Metacritic , IMDB and Rotten Tomatoes.

all_sites_score.csv and fandango_scrape.csv

Let's first explore the Fandango rating to see if our analysis agrees with the article's conclusion.

Finally let's begin to explore whether or not Fandango artificially displays higher ratings than warranted to boost ticket sales. Combining the Fandango Table with the ALL suts table. Not every movie in the fandango table is in the All sites table,Since some fandango movies have very little or no reviews.we only want to compare movies that are in both DataFrames, So doing an inner merge to merge togethher both DataFrames on the FILM columns.

Visualizing the distribution of ratings across all sites for the top 10 worst movies.

In this project we will Make Machine learnings Algorithm that will help us predict the risk of a Heart Attack a person have.

We will do use various Algorithms to predict the result and see which one suits best.

Data Analysis Feature Engineering Satandardization Model Building Predictions

• Importing Libraries and DataSet
• Data Analysis and Preprocessing
• Feature Engineering
• Model Building using ML
• Model Building and Prediction using H2O Auto ML

This database contains 14 physical attributes based on physical testing of a patient. Blood samples are taken and the patient also conducts a brief exercise test. The "goal" field refers to the presence of heart disease in the patient. It is integer (0 for no presence, 1 for presence). In general, to confirm 100% if a patient has heart disease can be quite an invasive process, so if we can create a model that accurately predicts the likelihood of heart disease, we can help avoid expensive and invasive procedures.

Content

Attribute Information:

age sex chest pain type (4 values) resting blood pressure serum cholestoral in mg/dl fasting blood sugar > 120 mg/dl resting electrocardiographic results (values 0,1,2) maximum heart rate achieved exercise induced angina oldpeak = ST depression induced by exercise relative to rest the slope of the peak exercise ST segment number of major vessels (0-3) colored by flourosopy thal: 3 = normal; 6 = fixed defect; 7 = reversable defect target:0 for no presence of heart disease, 1 for presence of heart disease Original Source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators:

Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.