Customer Intent Prediction

A machine learning project using Logistic Regression to predict customer purchase intent based on input features. This end-to-end pipeline covers data preprocessing, model training, evaluation, and deployment.

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🚀 Overview

Predicting customer purchase intent is essential for businesses aiming to understand behavior and boost sales. This project uses Logistic Regression, a binary classification technique, to model the probability of a customer taking an action, such as making a purchase or churning.

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⚙️ Pipeline Overview

The model pipeline follows a structured approach:

1. Data Preprocessing
2. Model Selection and Training
3. Hyperparameter Tuning
4. Model Evaluation
5. Deployment

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📊 1. Data Preprocessing

To prepare the raw data for modeling, the pipeline includes several key transformations:

• Handling Categorical Features:
  • One-Hot Encoding is applied to categorical features like product category and brand to convert them into numerical values.
• Standardizing Numerical Features:
  • Continuous features like customer age, purchase frequency, and satisfaction score are standardized to ensure zero mean and unit variance.
• Power Transformation:
  • A Box-Cox transformation is applied to features such as product price to normalize skewed distributions and improve model performance.

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🧠 2. Model Selection and Training

• The pipeline uses Logistic Regression, a powerful and interpretable binary classification algorithm.
• Objective: Model the probability of a customer performing a specific action (e.g., making a purchase) based on historical data and input features.
• Training: The preprocessed data is fed into the model, allowing it to learn relationships between features and the target variable.

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🔧 3. Hyperparameter Tuning

To optimize performance, key hyperparameters of the Logistic Regression model can be fine-tuned, such as:

• Solver (e.g., 'liblinear', 'saga')
• Regularization strength (C parameter)
• Maximum iterations

Fine-tuning ensures the model generalizes well to unseen data and minimizes overfitting.

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📈 4. Model Evaluation

The model's performance is assessed using key metrics:

• Accuracy: Overall correctness of predictions.
• Precision: Proportion of positive predictions that are correct.
• Recall: Ability to capture true positives.
• F1-Score: A balance between precision and recall.

The model is iteratively refined based on these metrics to achieve optimal results.

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🌐 5. Deployment

Once trained and evaluated, the model is ready for deployment in real-world applications:

• Integration: Embed the model into platforms such as web applications or customer analytics dashboards.
• Monitoring: Continuously track performance as new data becomes available and retrain when necessary.

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🛠 Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas, NumPy (Data manipulation)
  • Scikit-learn (Modeling and evaluation)
  • Matplotlib, Seaborn (Visualization)
• Tools: Jupyter Notebook

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📋 How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/Customer-Intent-Prediction.git  
   cd Customer-Intent-Prediction

2. Install dependencies:

   pip install -r requirements.txt  

3. Run the pipeline:

   python src/train_pipeline.py  

4. Evaluate results or view logs in models/.