Jiseong-Michael-Yang/Introduction-to-Machine-Learning

Assignments/project in Introduction to Machine Learning (Fall 2018)

Introduction to Machine Learning (DSC3006)

Assignments and project in Introduction to Machine Learning (Fall 2018)

1. Assignments

1. Assignment 1: Kaggle InClass Competition

• Goal
  The goal of assignment 1 is to practice data preprocessing and classification through a Kaggle InClass Competition. You are expected to understand how Kaggle works and how you can improve your classification model’s performance.

• Task
  You are provided with a classification dataset and your task is to build a series of models with the goal of improving the performance. You can use any data preprocessing technique and classification method.

• Data description

  • A detailed description is available at https://archive.ics.uci.edu/ml/datasets/student+performance.
  • The dataset used in the assignment 1 is a slightly modified version with 30 features and 1 categorical target variable. The goal is to use the 30 features and classify each student into one of the FIVE categories.
    • X_train.csv: 264 samples, 30 features (Id should not be counted as a feature)
    • y_train.csv: 264 samples, 1 target (from 1 to 5, each number represents a category)
    • X_test.csv: 131 samples, 30 features (the dataset you test your model)

Please refer to "~/Assignment1/Assignment1.pdf" for the datailed report.

2. Assignment 2: Ensemble Learning

• Goal
  The goal of assignment 2 is to practice ensemble learning.

• Task

  • Load the breast cancer wisconsin dataset from sklearn.

    from sklearn.datasets import load_breast_cancer
    data = load_breast_cancer()
    

  • Perform 5-fold cross validation, bagging and boosting.

  • Visualize the result.

  • Compare each methods.

Please refer to "~/Assignmnet2/Assignment2.pdf" for the datailed report.

2. Project: Lego Set Price Prediction (Multiple Linear Regressions & Neural Network)

1. Introduction

Kidults

• Kidults refers to grown-ups who still are consumers of toys.
• Kidult culture is rising as well as their power in the market.
• A marketing survey designed by myself to measure the kidults' satisfaction level on Lego's marketing efforts(product, price, place and promotion) had been conducted.

Survey Result

• Respondents' level of satisfaction on the product was quite fair given other areas and the average.
• However, they expressed a strong dissatisfaction on price-related marketing efforts of Lego with the score of 2.3 out of 5, which is notably lower than the average score of 3.17.
• This implies the gap between the percerived value and the price, probably caused by some outliers or unreasonably priced product set.

The Goal of the Project

Decrease the gap between the percerived value and the price by creating a model that is not or less affected by outliers.

2. Data Description

Please refer to '~/Final Project/Lego Set Price Prediction_Final_Team2.pdf' for the detailed project description.

• Dataset

  • "Are Lego Sets Too Pricey?", Jonathan Bouchet, Kaggle
  • 12261 rows with 14 columns
  • The dataset does not include the entire product lines.

• Features

  • Numerical Features
    • list_price [target]
    • num_reviews
    • piece_count
    • play_star_rating
    • star_rating
    • var_star_rating
    • prod_id
  • Categorical Features
    • ages
    • theme_name
    • review_difficulty
    • country
    • prod_desc
    • prod_log_desc
    • set_name

3. Exploratory Data Analysis (EDA)

• Histogram, Barplot and Swarmplot
  • 'ages' and 'list_price' are positively correlated.
  • 'review_difficulty'and 'list_price' are positively correlated.
  • There are considerable number of outliers.
• Correlation
  • 'piece_count' highly correlated with the 'list_price' positively.
  • Rating-related features are correlated with one another.

4. Data Preprocessing

• Remove Unnecessary Features
  • Product Descriptions (prod_desc & prod_log_desc)
  • Product ID (prod_id)
  • Set Names (set_name)
• Categorical Value Encoding
  • Cateogorical values were one-hot encoded.
  
• Grouping of Values
  • Country Names (country)
  • Theme Names (theme_name)
  • Age Range (ages)
• Outlier Removal
  • Outliers detected in the EDA stage has been removed with 'IsolationForest' from 'sklearn'
    
• Standardization
  • Numerical features has been normalized with 'StandardScaler' from 'sklearn'.
• Multi-collinearity Problem
  • VIF has greatly been stabilized due to the standardization.
    
• Data Split (Hold-Out)
  • The ratio of the train, validation, and test sets was 60%: 20%: 20%.

5. Model Fitting and Evaluation

• Models

  • Model with 94 features (full model)

    Please refer to '~/Final Project/Ver1_Full Model.ipynb'

  • Model with 22 features (reduced model 1)

    • Grouping of the features.

    Please refer to '~/Final Project/Ver2_Reduced Model1.ipynb'

  • Model with 3 features by PCA (reduced model 2)

    • Scree Plot
    

    Please refer to '~/Final Project/Ver3_Reduced Model2(PCA).ipynb'

• Ordinary Least Squares (OLS)

  • In Multiple Linear Regression (MLR) by OLS, R Squared, Adj. R Squared, F-statistics, and p-value were fair for all three models, though ones with more features displayed higher explanatory power.
  • Mean squared errors tend to be greater than that of neural network model.

• Neural Network (Tensorflow)

  • 2 hidden layers, 50 inputs between hidden layers, drop-out rate of 0.7, and 10,000 iterations.
  • Better performance compared to OLS in general.

• Least Absolute Shrinkage and Selection Operator (LASSO)

  • Best performance where alpha equals zero.
    

  • No need for regularization.

• Model Summary

  • Plots

    Number of Features	Residual Plot(MLR)	Predicted vs. Actual (NN)
    94
    22
    3

  • Mean Squared Error

    Number of Features	OLS	NN
    94	0.0821	0.0331
    22	0.1595	0.0492
    3	0.1022	0.0521

  • Efficiency-Accuracy Tradeoff

    • Full model performs better.
    • Full model recommended given sufficient computing power and time.

  6. Conclusion

  Significance

  • Setting a basis of Lego Set Price
  • Beneficial to both the customers and the manufacturers
  • Applicable to all consumer goods

  Limitaions

  • Data incompleteness of not covering all product lines, countries, and etc.
  • Better modeling will be possible with better dataset.