Machine Learning Final Project (Phase 1)

📌 Project Overview

This project is part of the Machine Learning course requirements. The objective is to apply the full machine learning pipeline on two different datasets:

• A Classification dataset
• A Regression dataset

Each dataset is processed through data exploration, preprocessing, visualization, modeling, and evaluation phases.

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📂 Datasets Used

1. Classification Dataset

• Name: Titanic Survival Dataset

• Source: Kaggle

• Description: This dataset contains information about passengers aboard the Titanic. The goal is to predict whether a passenger survived or not based on features such as age, gender, class, and fare.

• Target Variable: Survived (0 = No, 1 = Yes)

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2. Regression Dataset

• Name: Diamond Prices Dataset

• Source: Kaggle

• Description: This dataset includes various features related to diamonds, such as carat, cut, and clarity. The goal is to predict the price of the diamond.

• Target Variable: Price

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🔍 Project Phases

1. Data Retrieval

• Datasets were downloaded from Kaggle and Dryad
• Each dataset was inspected and described
• Columns and their meanings were analyzed

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2. Data Exploration

• Checked dataset structure (shape, data types)
• Identified missing values and duplicates
• Generated summary statistics
• Performed correlation analysis

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3. Data Preprocessing

• Handled missing values (imputation/removal)
• Removed duplicates and outliers
• Encoded categorical variables (e.g., One-Hot Encoding)
• Scaled numerical features
• Applied feature engineering where needed

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4. Data Visualization

• Used:

  • Matplotlib

• Created:

  • Histograms
  • Box plots

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5. Modeling

📊 Classification Models

• Decision Tree Classifier
• AdaBoost Classifier

📈 Regression Models

• Linear Regression
• Decision Tree Regressor
• AdaBoost Regressor

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6. Model Evaluation

Classification Metrics:

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

Regression Metrics:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

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🚀 How to Run the Project

1. Clone the repository
2. Run diamond_visualization and titanic_visualization for the plots and insights
3. Run preprocess_diamond and preprocess_titanic to preprocess the data before fitting into the models
4. Run each model file and review the output scores

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👥 Team Members

• Omar Elnainay
• Arsany Ehab
• Mariam Ahmed
• Ganna Mohamed
• Anas Radwan
• Marwan Amer

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📄 Submission Files

• 2 datasets (CSV format)
• 2 preprocessing files (.py)
• 2 visualization files (.py)
• 3 classification models (.py)
• 2 regression models (.py)
• Final report (.pdf)
• README.md

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💡 Notes

• Proper preprocessing significantly improves model performance
• Feature engineering plays an important role in both datasets
• Model comparison helps in selecting the best-performing algorithm

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✅ Conclusion

This project demonstrates the application of machine learning techniques on both classification and regression problems. It highlights the importance of data preprocessing, model selection, and evaluation in building effective predictive models.

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