📢 Product Ad Campaign Performance Prediction

Data-Driven Analysis & Machine Learning for Marketing Performance Optimization

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📌 Overview

Digital advertising campaigns generate large volumes of data, but raw data alone does not guarantee actionable insights.
This project focuses on analyzing and predicting product advertisement campaign performance using data-driven techniques to help businesses make informed marketing decisions.

The goal is to evaluate campaign effectiveness and identify patterns that influence engagement and performance outcomes.

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🎯 Problem Statement

Marketing teams often struggle to understand:

• Which campaigns perform well
• What factors drive user engagement
• How to optimize ad spend effectively

Without data-backed insights, decisions may rely on assumptions rather than measurable performance indicators.

Objective:
Build a predictive and analytical framework to assess product ad campaign performance and extract meaningful business insights.

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📊 Dataset Overview

• Campaign-level performance data
• Includes engagement, reach, and conversion-related attributes
• Structured dataset suitable for analysis and modeling

Dataset details are kept concise to emphasize analytical approach and insights.

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🧠 Approach & Methodology

1️⃣ Exploratory Data Analysis (EDA)

• Campaign performance distribution analysis
• Identification of high- and low-performing campaigns
• Correlation analysis between features

2️⃣ Data Preprocessing

• Handling missing values
• Encoding categorical variables
• Feature scaling and transformation

3️⃣ Feature Engineering

• Creation of performance-related indicators
• Identification of impactful campaign attributes

4️⃣ Model Development

• Trained machine learning models to predict campaign performance
• Model evaluation using appropriate performance metrics

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🤖 Models Used

• Supervised machine learning algorithms
• Model selection based on interpretability and performance

(Exact models and metrics are documented in the notebook)

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📈 Results & Business Insights

• Identified key factors influencing ad performance
• Highlighted patterns linked to higher engagement and conversions
• Provided insights that can support:
  • Budget allocation
  • Campaign optimization
  • Marketing strategy decisions

📌 Detailed analysis and results are available in the notebooks.

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🛠️ Tech Stack

• Programming Language: Python
• Data Analysis: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Modeling: Scikit-learn
• Reporting: Jupyter Notebook

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▶️ How to Run the Project

Step 1: Install Dependencies

pip install -r requirements.txt

© 2025 Rachit Patwa. All rights reserved.