Models made while progressing through IBM open courseware and other personal projects.
Course info can be found here.

Table of Contents

• Supervised Learning
  • Regression
    • Simple Linear Regression
    • Non Linear Regression
  • Classification
    • Decision Tree
    • K-Nearest Neighbors
    • Logistic Regression
    • Support Vector Machine
    • Naive Bayes
• Unsupervised Learning
  • Clustering
    • K-Means
    • Agglomerative Hierarchical
    • DBSCAN
  • Recommender Systems
    • Content-based

Supervised Learning

Regression

Simple Linear Regression

• Student's Final Grade Predictor
  Uses Student Performance Data Set from UCI ML Repository.

Non-Linear Regression

• GDP Predictor
  Uses yearly GDP data. Can be obtained from various sources such as The World Bank.

Classification

Decision Tree

• Iris Species Decision Tree
  Uses iris dataset
  Model Accuracy: 0.977
  

K-Nearest Neighbors

• Iris Species Identification using KNN
  Uses iris dataset
  The best accuracy was with 1.0 with k = 8
  

Logistic Regression

• Iris Species Identification using Logistic Regression
  Uses iris dataset

Prediction using Raw Data

Logloss = 0.863

Classification Report
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        11
Iris-versicolor       0.00      0.00      0.00        13
 Iris-virginica       0.32      1.00      0.48         6

       accuracy                           0.57        30
      macro avg       0.44      0.67      0.49        30
   weighted avg       0.43      0.57      0.46        30

Confusion Matrix

Prediction using Normalized Data

Logloss = 0.855

Classification Report
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        11
Iris-versicolor       1.00      0.23      0.38        13
 Iris-virginica       0.38      1.00      0.55         6

       accuracy                           0.67        30
      macro avg       0.79      0.74      0.64        30
   weighted avg       0.88      0.67      0.64        30

Confusion Matrix

Support Vector Machine

• Iris Species Identification using various SVM kernels
  Uses iris dataset
  Best accuracy obtained using SVM linear kernel with 0.85 accuracy score

linear Classifcation Report

                 precision    recall  f1-score   support

Iris-Versicolor       0.82      0.90      0.86        10
 Iris-Virginica       0.89      0.80      0.84        10

       accuracy                           0.85        20
      macro avg       0.85      0.85      0.85        20
   weighted avg       0.85      0.85      0.85        20

poly Classification Report

                 precision    recall  f1-score   support

Iris-Versicolor       0.75      0.90      0.82        10
 Iris-Virginica       0.88      0.70      0.78        10

       accuracy                           0.80        20
      macro avg       0.81      0.80      0.80        20
   weighted avg       0.81      0.80      0.80        20

rbf Classification Report

                 precision    recall  f1-score   support

Iris-Versicolor       0.75      0.90      0.82        10
 Iris-Virginica       0.88      0.70      0.78        10

       accuracy                           0.80        20
      macro avg       0.81      0.80      0.80        20
   weighted avg       0.81      0.80      0.80        20

Sigmoid Classification Report

                 precision    recall  f1-score   support

Iris-Versicolor       0.50      1.00      0.67        10
 Iris-Virginica       0.00      0.00      0.00        10

       accuracy                           0.50        20
      macro avg       0.25      0.50      0.33        20
   weighted avg       0.25      0.50      0.33        20

Confusion Matrix

Naive Bayes

• Spam/ Ham Classification (py, ipynb)
  Uses SMS Spam Collection Data Set from UCI (Original source, dataset raw file)
  Wordclouds
  
  Evaluation: Classification Report, Log loss, Matthew Correlation Coefficient and Confusion Matrix

              precision    recall  f1-score   support

         ham       0.97      1.00      0.99       976
        spam       0.99      0.81      0.89       139

    accuracy                           0.98      1115
   macro avg       0.98      0.91      0.94      1115
weighted avg       0.98      0.98      0.97      1115

Log loss: 0.836
Matthews Correlation Coefficient: 0.885

Confusion Matrix

Unsupervised Learning

Clustering

K-Means - Iris clustering

• Iris Species Clustering
  Uses iris dataset
  k = 3 clusters gives the best Rand Index score at 0.73
  This evaluation method is possible since the original label (Species column) was retained as label_true, and comparison were made between label_pred and label_true using rand index.
  

Optimization using elbow methods were also performed using both distortion and inertia.
Both methods confirm the best cluster is k = 3.

K-Means - Compressing Image Color

• Compressing Image Color
  Uses K Means clustering to reduce the original color scale to predefined clusters.

Agglomerative Hierarchical

• Iris Species Clustering
  Uses iris dataset
  

iris.groupby(['cluster_', 'Species'])["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"].mean()

 	                         SepalLengthCm 	SepalWidthCm 	PetalLengthCm 	PetalWidthCm
cluster_ 	Species
0 	       Iris-setosa 	    5.006000 	3.418000 	1.464000 	0.244000

1 	       Iris-versicolor 	6.700000 	3.000000 	5.000000 	1.700000
               Iris-virginica 	6.893939 	3.118182 	5.806061 	2.133333

2 	       Iris-versicolor 	5.920408 	2.765306 	4.244898 	1.318367
               Iris-virginica 	5.994118 	2.694118 	5.058824 	1.817647

Evaluation using Species column as ground truth:
Homogeneity Score: 0.744
Adjusted Mutual Info Score: 0.753
Normalized Mutual Info Score: 0.756
V-measure Score: 0.756

DBSCAN

• Iris Species Clustering
  Uses iris dataset
  

Evaluation using Species column as ground truth:
Estimated number of clusters: 2
Estimated number of noise points: 3
Homogeneity: 0.576
Completeness: 0.877
V-measure: 0.696
Adjusted Rand Index: 0.554
Adjusted Mutual Info: 0.690
Silhouette Coefficient: 0.555

Recommender Systems

Content-based

• Recomendating restaurants based on user past rating history
• Selected feature is the cuisine type
• Datasets as provided here. (Click here to navigate to original source)
• Future improvement:
  • using knn to classify restaurant by cuisine type and use it as ground truth for evaluation
  • adding/ incorporating other rating criterias to get a more solid user profile (only food_rating was considered in the existing built model)
  • Somehow, all of the recommended placeID obtained from the model is not in geospatial2.csv file provided from the source (which I had assumed to contain all of the restaurants info).. Still unsure if this is a bug..
• Output: df containig topN of recommended placeID & its weighted recommendation score for the specified userID

get_recommendation("U1138")

Rcuisine 	total_by_place
placeID 	
132774 	7
135099 	6
135098 	4
135103 	4
135097 	4

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