I start by importing the reviews dataset in WEKA, then I perform some text preprocessing tasks such as word extraction, stop-words removal, stemming and term selection. Finally, I run various classification algorithms naive bayes, k-nearest neighbors and I compare the results, in terms of classification accuracy.
Below, I will proceed from a simple linear regression to a generalized additive model to an ordered logistic regression analysis. And I will illustrate the results with nice plots along the way.
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Of course, all done in R you can get the script here. Data The data for this little project comes from the IMDb website and, in particular, from my personal ratings of titles recorded there.
IMDb keeps the movies you have rated in a nice little table which includes information on the movie title, director, duration, year of release, genre, IMDb rating, and a few other less interesting variables.
Conveniently, you can export the data directly as a csv file. Outcome variable The outcome variable that I want to predict is my personal movie rating.
IMDb lets you score movies with one to ten stars. Half-points and other fractions are not allowed. It is a tricky variable to work with.
It is obviously not a continuous one; at the same time ten ordered categories are a bit too many to treat as a regular categorical variable.
The mean of my ratings is a good 0. Data-generating process Some reflection on how the data is generated can highlight its potential shortcomings.
First, life is short and I try not to waste my time watching bad movies. Second, even if I Data mining project on imdb website fooled to start watching a bad movie, usually I would not bother rating it on IMDb.
The data-generating process leads to a selection bias with two important implications. First, the effective range of variation of both the outcome and the main predictor variables is restricted, giving the models less information to work with.
Second, because movies with a decent IMDb ratings which I disliked have a lower chance of being recorded in the dataset, the relationship we find in the sample will overestimate the real link between my ratings and the IMDb ones. An ordinary linear regression model is a common starting point for analysis and its results can serve as a baseline.
Here are the estimates that lm provides for regressing my ratings on IMDb scores: The positive coefficient of IMDb score is positive and very close to one which implies that one point higher lower IMDb rating would predict, on average, one point higher lower personal rating.
Figure 2 plots the relationship between the two variables for an interactive version of the scatter plot, click here: The solid black line is the regression fit, the blue one shows a non-parametric loess smoothing which suggests some non-linearity in the relationship that we will explore later.
Although the IMDb score coefficient is highly statistically significant that should not fool us that we have gained much predictive capacity. The model fit is rather poor. The root mean squared error is 1. But the inadequate fit is most clearly visible if we plot the actual data versus the predictions.
Figure 3 below does just that. If the predictions derived from the model were good, the dots observations would be very close to the diagonal indicated by the dotted line. In this case, they are not. The model does a particularly bad job in predicting very low and very high ratings.
We can also see how little information IMDb scores contain about my personal scores by going back to the raw data.
Figure 4 plots to density of my ratings for two sets of values of IMDb scores — from 6. The means for the two sets differ somewhat, but the overlap in the density is great. Some playing around shows that among the available candidates only the year of release of the movie and dummies for a few genres and directors selected only from those with more than four movies in the data give any leverage.
The root mean squared error of this model is 1. Moreover, looking again at the actual versus predicted ratings, the fit is better, especially for highly rated movies — no surprise given that the director dummies pick these up.
The last variable in the regression above is the year of release of the movie. It is coded as the difference fromso the positive coefficient implies that older movies get higher ratings.
The statistically significant effect, however, has no straightforward predictive interpretation. The reason is again selection bias. I have only watched movies released before the s that have withstood the test of time.Prof.
Galit Shmueli, Institute of Service Science, College of Technology Management, National Tsing Hua University, Kuang Fu Road Sec.
2, Hsinchu Taiwan. How to scrape imdb webpage? This project is part of a learning endeavor on web scraping and hence all these troubles. -:) – user May 1 '15 at add a comment | Browse other questions tagged data-mining python scraping or ask your own question.
3 . Data Mining Project on IMDB website ABSTRACT The Internet Movie Database (IMDb) is an online database of information related to movies, television shows, stars, etc.
We chose to do our project from to year’s movie database. We extracted data like Movie, Director, Star, Image Url, Studio from the IMDb website. to transform the IMDb data into a format suitable for data mining, and provides a selection of information mined from this refined data, in section Experimental results.
let me clear you some important things before you start your data mining project. * First learn scikit-learn you can visit the website caninariojana.com * once you learn some basic on scikit-learn website now you are in a position to think data.
Mar 15, · Project on Movie ratings and release dates. Used IMDB ratings for 7 years, Spreadsheet, R to cluster.