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ML Based Customer Purchase Prediction Model

Project Overview

A machine learning project predicting customer purchase behavior using multiple classification algorithms.

Dataset Description

  • Total Rows: 10,000
  • Total Columns: 9

Columns

  1. customer_id: Unique customer identifier
  2. age: Customer's age
  3. gender: Customer's gender
  4. annual_income: Customer's annual income
  5. last_visited_days_ago: Days since last website visit
  6. session_duration: Time spent on website
  7. pages_visited: Number of pages browsed
  8. device: Device used for browsing
  9. purchase: Target variable (Purchase/No Purchase)

Sample Data

Below is a sample dataset that provides customer details, session activity, and purchase behavior:

customer_id age gender annual_income last_visited_days_ago session_duration pages_visited device purchase
1 56 male 7 17 15 desktop 0
2 69 female 47617 4 35 19 mobile 0
3 46 male 94258 30 15 mobile 0
4 32 female 70075 19 4 12 mobile 0
5 60 male 146998 16 51 mobile 0
6 25 male 42631 8 31 16 desktop 1
7 38 female 143120 31 6 desktop 1
8 56 male 117158 24 9 20 tablet 0
9 36 female 158955 12 31 15 desktop 0

Model Performance

  • Logistic Regression: 48% accuracy
  • Random Forest: 70% accuracy
  • Gradient Boosting: 70% accuracy

Final Model

  • Selected Model: Random Forest
  • Best Accuracy: 70%

Requirements

  • Python 3.8+
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn

Installation

  1. Clone the repository
git clone https://github.com/PRANAYBHUMAGOUNI/ML-Based-Customer-Purchase-Prediction-Model/
  1. Create virtual environment
python -m venv venv
venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt

Example Prediction

new_customer = pd.DataFrame({
    'age': [69],
    'gender': ['female'],
    'annual_income': [47617],
    'last_visited_days_ago': [4],
    'session_duration': [35],
    'pages_visited': [19],
    'device': ['mobile']
})

# Prediction
prediction = model.predict(new_customer)
probability = model.predict_proba(new_customer)

Prediction Result

  • Prediction: Will not purchase
  • Purchase Probability: 21.48%

Feature Engineering

  1. days_since_visit_ratio: Ratio of days since last visit to session duration
  2. pages_per_minute: Pages visited per minute
  3. income_age_ratio: Annual income divided by age
  4. engagement_score: Combination of pages visited and session duration

Preprocessing Steps

  • Handled missing values
  • Encoded categorical variables
  • Created engineered features
  • Scaled numerical features

Hyperparameter Tuning

  • Used GridSearchCV for Random Forest and Gradient Boosting
  • Explored parameters:
    • Number of estimators
    • Max depth
    • Min samples split
    • Learning rate

Future Improvements

  • Collect more diverse data
  • Experiment with advanced ensemble methods
  • Incorporate more complex feature engineering
  • Try deep learning approaches