Predicting Return Shifts in Arbitrage Click Trading

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In this project, I developed a predictive model for arbitrage click trading in digital marketing campaigns. The model forecasts the probability of returns shifting from positive to negative based on historical CPC and RPC data. The goal is to optimize investment decisions and minimize losses.

Campaign Analysis

The Core Challenges

  • Data Volatility:

    PC and RPC values can fluctuate wildly, making it hard to predict future returns accurately.

  • Feature Selection:

    Identifying which past data points are most useful for making accurate predictions..

  • Model Accuracy:

    Ensuring the model accurately predicts return shifts can be difficult due to the complex patterns in the data.

Solution

To tackle the challenges in predicting return shifts, I employed a methodical approach involving feature engineering, model testing, and parameter tuning. By splitting the data into test and validation sets, I ensured the selection of the most accurate model for reliable predictions.

  • Feature Engineering:

    Developed features such as lag and moving averages for CPC, RPC, and return to capture trends and patterns effectively.

  • Model Selection:

    Evaluated multiple models to identify the one with the best accuracy for predicting return shifts, ensuring reliable forecasts.

  • Parameter Tuning:

    Optimized model parameters to enhance prediction accuracy and performance.

  • Data Splitting:

    Divided the dataset into test and validation sets to select and validate the best-performing model.

Using a Variety of Data Sources to Gain Better Understanding

This data integration process involved consolidating a single CSV file, ensuring a comprehensive and cohesive dataset for analysis. By utilizing this CSV file, I streamlined the data into a unified format, enhancing the client's ability to derive actionable insights and make informed decisions based on a well-organized dataset.

  • CSV

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Steps Followed By Me

Import Data

Data Import

Imported the dataset from a single CSV file to start the analysis.

Summarize Data

Data Understanding

Reviewed the dataset to gain insights into its structure and content.

Column Description

Data Assessment

Identified and addressed issues within the data to ensure quality and resolve them.

Assess Data

Feature Engineering

Created new features like lag and moving averages to improve model performance.

Clean Data

Model Selection

Trained and tested various models, selecting the one with minimal difference between test and validation accuracy.

Visualize Data

Probability Prediction

Developed a function to predict the next day's probability based on campaign ID, aiding in investment decisions.