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Case Study: Predicting Traffic Patterns with Machine Learning

A data analytics approach to Billboard Pricing

The Challenge Our client, a billboard management company, needed to gain insights into future traffic patterns using historical data to price the billboards based on "eyeballs". However, they lacked the expertise and resources to analyze the vast amount of data they had collected. The Solution We proposed using machine learning to analyze the historical traffic data and build a predictive model that could accurately forecast future traffic patterns. To achieve this, we chose to use Random Forests, a machine learning algorithm that works well with large and complex datasets. First, we collected and cleaned the historical traffic data, including variables such as time of day, day of week, weather conditions, and previous vehicle counts. We then used this data to train the Random Forest model to predict the vehicle count for the next 24 hours at specific locations. To evaluate the model's accuracy, we tested it on a holdout dataset and achieved a prediction accuracy of over 90% for all the locations. The Impact Our solution provided our client with a powerful tool for estimating the actual number of eyeballs. By accurately predicting future traffic patterns, they could offer more transparent pricing in an otehrwise opaque industry. Our work with this client has also opened up opportunities for similar predictive analytics projects in other industries.

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