🚀 Machine Learning Projects Using Random Forest Classifier – End-to-End Use Cases

This repository contains end-to-end Machine Learning projects demonstrating strong fundamentals in EDA, feature engineering, model building, evaluation, and business impact analysis using Python.

Each project follows the Machine Learning Life Cycle and is documented with clear objectives, insights, and outcomes.

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📌 Project 1: Customer Purchase Prediction using Random Forest

(Company Data Analysis)

🎯 Objective

To predict whether a customer is likely to make a purchase based on demographic and behavioral attributes, enabling businesses to improve targeting and marketing strategies.

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🧠 Problem Statement

Businesses often struggle to identify customers who are most likely to convert. An automated predictive model helps optimize marketing spend and improve conversion rates by targeting high-probability customers.

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🛠️ Solution Approach

• Performed Exploratory Data Analysis (EDA) to understand customer behavior
• Applied feature encoding and preprocessing
• Built Random Forest classification model to predict purchase likelihood
• Evaluated model using appropriate performance metrics

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📊 Key Insights

• Age and salary significantly influence purchase decisions
• Certain demographic segments show higher conversion probability
• Model helps in identifying high-value customers effectively

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💼 Business Impact

• Improved marketing efficiency
• Better customer segmentation
• Data-driven decision making for campaigns
• Reduced customer acquisition cost

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🧰 Tools & Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

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📌 Project 2: Fraud Risk Detection using Random Forest

(Fraud Check Analysis)

🎯 Objective

To classify individuals as “Risky” or “Good” based on financial and demographic attributes using a Random Forest Classifier.

Business Rule Applied:

• Taxable Income ≤ 30000 → Risky
• Taxable Income > 30000 → Good

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🧠 Problem Statement

Manual fraud detection is inefficient and prone to errors. Organizations require a scalable and automated system to identify high-risk individuals early to reduce financial losses.

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🛠️ Solution Approach

• Created target variable using domain-specific business rules
• Conducted separate EDA notebook with detailed visual analysis
• Encoded categorical variables
• Built and evaluated a Random Forest classification model
• Analyzed feature importance for interpretability

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📊 Key Insights

• Taxable income is the strongest indicator of fraud risk
• Lower work experience correlates with higher risk
• City population and demographic attributes also influence risk
• Random Forest handled non-linear relationships effectively

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💼 Business Impact

• Early detection of high-risk individuals
• Reduction in fraud-related financial losses
• Improved compliance and monitoring
• Scalable and automated fraud screening solution

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🧰 Tools & Technologies Used

• Python
• Pandas, NumPy
• Seaborn, Matplotlib
• Scikit-learn (Random Forest)
• Jupyter Notebook

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📂 Repository Structure

├── company_data_prj_9.ipynb        # Customer Purchase Prediction Project
├── fraud_check_prj_10.ipynb        # Fraud Risk Detection Project
├── README.md                       # Project documentation