MovieRent Predictor

1. SQL script
2. Python script

About the Project

The objective of this project is to determine the probability of a movie to being rented again based on a collection of over one-year historical data (from 2005/05/24 to 2006/02/14).

This reposatory is build upon my previous knowledge in Linear Regression, to do a Logistic Regression model for predictive analysis.

About the Dataset

For a complete description of the dataset extraction process, please refer to the SQL script. Also, to read all documentation regarding feture selection and creating the target refer to the Python script.

Improvements from previous project

• Organization: We have two separate scripts for SQL and Python.
• SQL: Not my strongest point. For this project, it was important to decide whether RIGHT, LEFT or INNER JOIN to not loose data.
• Entropy-Bonus: We fixed a GitHub formatting issue that displayed code horizontally due to HTML boxes.

Changes (28/03/23)

• Structure: Small changes for a better structure.
• Libraries: We reduced the number.
• Roc curve plot: Up2date. Ty, Nati!.
• LEFT JOIN: Fixed. We use an INNER JOIN instead. It made no sense to extract films with 0 rentals.
• Treshold: Last time was defined by the mean. Now it's by the median since our target movie_demand has a different distribution.
• Normalization: This time we will normalize numericals with StandScaler.
• Encoding: Get dummies instead of LabelEncoder.
• Results: Better accuaracy (99% compared to our previous 92%) and confussion matrix results.

Changes (06/04/2023)

• SQL: Small changes.
• Python: We used color headers, inspired by Nara.
• Functions: Imported from a library.
• Documentation: Small changes, also added a table with each column and it's description in step 01. Inspired by Luis.
• Encoding numericals: Current knowledge. We did an extra ordinal encoding (again, ty Luis) to ratings and we cleaned some numericals.
• NaN's: We got many after creating each dataset. To solve it we reseted the index in some cases and in others we did fillna.
• Metrics: Added. Before we did not know how to read the results appart from accuracy. Now we added recall, precision, f1-score, macro avg and weighted avg

Model Results

Roc curve:

Confussion matrix:

Our model has:

• 99 True Negatives Vs 39 False Positive
• & 88 True Positives Vs 31 False Negatives
• The model is slightly better predicting True Negatives rather than positives.
• Our precision is balanced in both low (0) or high demand (1) and recall has a higher score for low demand movies.

Comparision with previous attemp (unrealistic)

Testing the Model

Top 15 movie rentals

So, for example, film_id 1000 is a movie with high demand, and we should keep more copies to our inventory.

To find a name of the film, we simply do SELECT * FROM film WHERE film_id = 1000 in SQL or Python.

In this case, 1000 corresponds to ZORRO ARK.

Less rented film

And, film_id 400 had been rented 4 times during 2005/05/24 and it's not expected to be in demanand. Again, to get the name of the film; SELECT * FROM film WHERE film_id = 400:

In this case, 400 corresponds to HARDLY ROBBERS.

Tools

Enviornments

• JupyterLab (mostly) & JupyterNotebook

Libraries

• Data manipulation: pandas
• Numerical operations: numpy
• Enhanced EDA: ydata profiling
• Visualization: matplotlib, seaborn
• Settings: warnings
• SQL connection: getpass, create_engine
• Machine Learning: scikit-learn
• Skewness: skew
• Preprocessing: StandScaler
• Model selection: train test split
• Logistic model: LogisticRegression
• Metrics evaluation: roc curve, confusion matrix & classification report
• Confussion Displayer: ConfusionMatrixDisplay