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+# Email Spam Filter
+
+This project creates an email spam filter based on supervised learning that classifies emails as either spam (unwanted) or ham (legitimate) for my data analysis and vsiualization class.
+
+I used two supervised learning algorithms, K Nearest Neighbors (KNN) and Naive Bayes, and compared their performances. To train and evaluate these classifiers, I used the Enron spam email dataset, which consists of approximately 34,000 emails. Once the classifiers were trained, I ran them in a Jupyter Notebook to predict whether new emails are spam or ham.
+
+## Goals
+
+- Explore and implement the KNN and Naive Bayes algorithms.
+- Gain hands-on experience in preprocessing text data, specifically converting emails into numeric features suitable for model processing.
+- Set up a supervised learning problem and analyze the results.
+- Understand and follow a typical end-to-end supervised machine learning workflow.
+- Work with a large, real text dataset.
+
+## Dataset
+
+I used the Enron spam email dataset for this project. You can download the dataset using the following links:
+
+- [Enron Emails Dataset - Dev Set](https://cs.colby.edu/courses/S23/cs251/projects/p6spam/data/enron_dev.zip)
+- [Enron Emails Dataset](https://cs.colby.edu/courses/S23/cs251/projects/p6spam/data/enron.zip)
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diff --git a/data/spiral_train_2.csv b/data/spiral_train_2.csv
new file mode 100644
index 0000000..351df30
--- /dev/null
+++ b/data/spiral_train_2.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/spiral_val_1.csv b/data/spiral_val_1.csv
new file mode 100644
index 0000000..fbd7518
--- /dev/null
+++ b/data/spiral_val_1.csv
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diff --git a/data/spiral_val_2.csv b/data/spiral_val_2.csv
new file mode 100644
index 0000000..61e9fbc
--- /dev/null
+++ b/data/spiral_val_2.csv
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diff --git a/data/winequality-red.csv b/data/winequality-red.csv
new file mode 100644
index 0000000..ee44f08
--- /dev/null
+++ b/data/winequality-red.csv
@@ -0,0 +1,1601 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality
+numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric
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\ No newline at end of file
diff --git a/email_preprocessor.py b/email_preprocessor.py
new file mode 100644
index 0000000..5dbffd8
--- /dev/null
+++ b/email_preprocessor.py
@@ -0,0 +1,195 @@
+"""email_preprocessor.py
+Preprocess Enron email dataset into features for use in supervised learning algorithms
+"""
+import re
+import os
+import numpy as np
+
+
+def tokenize_words(text):
+    """Transforms an email into a list of words.
+
+    Parameters:
+    -----------
+    text: str. Sentence of text.
+
+    Returns:
+    -----------
+    Python list of str. Words in the sentence `text`.
+
+    """
+    # Define words as lowercase text with at least one alphabetic letter
+    pattern = re.compile(r"[A-Za-z]+[\w^\']*|[\w^\']*[A-Za-z]+[\w^\']*")
+    return pattern.findall(text.lower())
+
+
+def count_words(email_path="data/enron"):
+    """Determine the count of each word in the entire dataset (across all emails)
+
+    Parameters:
+    -----------
+    email_path: str. Relative path to the email dataset base folder.
+
+    Returns:
+    -----------
+    word_freq: Python dictionary. Maps words (keys) to their counts (values) across the dataset.
+    num_emails: int. Total number of emails in the dataset.
+    """
+    count = 0
+    word_freq = {}
+
+    for dirpath, dirnames, filenames in os.walk(email_path):
+        for i in filenames:
+            if ".txt" in i:
+                filepath = os.path.join(dirpath, i)
+                file = open(filepath, encoding="latin-1").read()
+                words = tokenize_words(file)
+                for word in words:
+                    if word in word_freq:
+                        word_freq[word] += 1
+                    else:
+                        word_freq[word] = 1
+                count += 1
+
+    return word_freq, count
+
+
+def find_top_words(word_freq, num_features=200):
+    """Given the dictionary of the words that appear in the dataset and their respective counts,
+    compile a list of the top `num_features` words and their respective counts.
+
+    Parameters:
+    -----------
+    word_freq: Python dictionary. Maps words (keys) to their counts (values) across the dataset.
+    num_features: int. Number of top words to select.
+
+    Returns:
+    -----------
+    top_words: Python list. Top `num_features` words in high-to-low count order.
+    counts: Python list. Counts of the `num_features` words in high-to-low count order.
+    """
+
+    words = []
+    counts = []
+
+    frequencies = sorted(word_freq, key=word_freq.get, reverse=True)
+
+    for i in range(num_features):
+        if i < len(frequencies):
+            curr = frequencies[i]
+            words.append(curr)
+            counts.append(word_freq[curr])
+
+    return words, counts
+
+
+def make_feature_vectors(top_words, num_emails, email_path="data/enron"):
+    """Count the occurance of the top W (`num_features`) words in each individual email, turn into
+    a feature vector of counts.
+
+    Parameters:
+    -----------
+    top_words: Python list. Top `num_features` words in high-to-low count order.
+    num_emails: int. Total number of emails in the dataset.
+    email_path: str. Relative path to the email dataset base folder.
+
+    Returns:
+    -----------
+    feats. ndarray. shape=(num_emails, num_features).
+        Vector of word counts from the `top_words` list for each email.
+    y. ndarray of nonnegative ints. shape=(num_emails,).
+        Class index for each email (spam/ham)
+    """
+
+    labels = np.zeros(num_emails)
+    word_freq = np.zeros((num_emails, len(top_words)))
+
+    index = 0
+
+    for dirpath, dirnames, filenames in os.walk(email_path):
+        for i in filenames:
+            if ".txt" in i:
+                filepath = os.path.join(dirpath, i)
+                file = open(filepath, encoding="latin-1").read()
+                words = tokenize_words(file)
+
+                if "spam" in filepath:
+                    labels[index] = 0
+                else:
+                    labels[index] = 1
+
+                for j in range(len(top_words)):
+                    word_freq[index, j] = words.count(top_words[j])
+
+                index += 1
+
+    return word_freq, labels
+
+
+def make_train_test_sets(features, y, test_prop=0.2, shuffle=True):
+    """Divide up the dataset `features` into subsets ("splits") for training and testing. The size
+    of each split is determined by `test_prop`.
+
+    Parameters:
+    -----------
+    features. ndarray. shape=(num_emails, num_features).
+        Vector of word counts from the `top_words` list for each email.
+    y. ndarray of nonnegative ints. shape=(num_emails,).
+        Class index for each email (spam/ham)
+    test_prop: float. Value between 0 and 1.  The proportion of the dataset to use for testing.
+    shuffle: boolean. Whether or not to shuffle the dataset before splitting.
+
+    Returns:
+    -----------
+    x_train: ndarray. shape=(num_train_samps, num_features).
+        Training dataset
+    y_train: ndarray. shape=(num_train_samps,).
+        Class values for the training set
+    inds_train: ndarray. shape=(num_train_samps,).
+        The index of each training set email in the original unshuffled dataset.
+    x_test: ndarray. shape=(num_test_samps, num_features).
+        Test dataset
+    y_test:ndarray. shape=(num_test_samps,).
+        Class values for the test set
+    inds_test: ndarray. shape=(num_test_samps,).
+        The index of each test set email in the original unshuffled dataset.
+    """
+    inds = np.arange(y.size)
+    if shuffle:
+        features = features.copy()
+        y = y.copy()
+
+        inds = np.arange(y.size)
+        np.random.shuffle(inds)
+        features = features[inds]
+        y = y[inds]
+
+    train = int(features.shape[0] * (1 - test_prop))
+
+    x_train = features[:train, :]
+    y_train = y[:train]
+    inds_train = inds[:train]
+
+    x_test = features[train:, :]
+    y_test = y[train:]
+    inds_test = inds[train:]
+
+    return x_train, y_train, inds_train, x_test, y_test, inds_test
+
+
+def retrieve_emails(inds, email_path="data/enron"):
+    """Obtain the text of emails at the indices `inds` in the dataset.
+
+    Parameters:
+    -----------
+    inds: ndarray of nonnegative ints. shape=(num_inds,).
+        The number of ints is user-selected and indices are counted from 0 to num_emails-1
+        (counting does NOT reset when switching to emails of another class).
+    email_path: str. Relative path to the email dataset base folder.
+
+    Returns:
+    -----------
+    Python list of str. len = num_inds = len(inds).
+        Strings of entire raw emails at the indices in `inds`
+    """
+    pass
diff --git a/knn.ipynb b/knn.ipynb
new file mode 100644
index 0000000..5a284cd
--- /dev/null
+++ b/knn.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NARIT TRIKASEMSAK**\n",
+    "\n",
+    "Spring 2023\n",
+    "\n",
+    "CS 251/2: Data Analysis and Visualization\n",
+    "\n",
+    "Project 6: Supervised learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "plt.style.use(['seaborn-v0_8-colorblind', 'seaborn-v0_8-darkgrid'])\n",
+    "plt.rcParams.update({'font.size': 20})\n",
+    "\n",
+    "np.set_printoptions(suppress=True, precision=5)\n",
+    "\n",
+    "# Automatically reload external modules\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Project 6) Supervised learning\n",
+    "\n",
+    "The overall goal of this project is to implement an email spam filter to determine whether an email is spam (*spam*) or not (*ham*). You will implement and compare the performance of two supervised learning algorithms: **K Nearest Neighbors (KNN)** and **Naive Bayes**."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1: K Nearest Neighbors (KNN) Classifier\n",
+    "\n",
+    "To start off the project, you will implement the **KNN classifier**, a bedrock, highly-versatile, nonparametric (i.e. *memory-based*) supervised learning algorithm. You will test out and experiment with KNN on a **multi-class spiral 2D dataset**."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1a) Load and visualize spiral data\n",
+    "\n",
+    "- Below, load in both spiral datasets 1 (`spiral_train_1.csv`, `spiral_val_1.csv`) and 2 (`spiral_train_2.csv`, `spiral_val_2.csv`). Each training set has 4,000 samples and each validation set has 1,200 samples (*there is no test set for this development dataset*).\n",
+    "- Create a 2x2 grid plot showing the train and validation data side-by-side in each version of the dataset.\n",
+    "    - Be sure to label your subplots with informative titles (which datset are we looking at?).\n",
+    "    - Color-code the points based on their class.\n",
+    "    - Set the figure size to make everything clearly legible (not microscopic).\n",
+    "\n",
+    "#### Format of spiral data\n",
+    "\n",
+    "- Column 1: x coordinate of a 2D point (on a spiral).\n",
+    "- Column 2: y coordinate of a 2D point (on a spiral).\n",
+    "- Column 3: class. Which spiral arm does the point belong to? Labels: [0, 1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Spiral 1 train (4000, 2), classes (4000,)\n",
+      "Spiral 1 validation (1200, 2), classes (1200,)\n",
+      "Spiral 2 train (4000, 2), classes (4000,)\n",
+      "Spiral 2 validation (1200, 2), classes (1200,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "spiral_1_train = np.loadtxt('data/spiral_train_1.csv', skiprows=1, delimiter=',')\n",
+    "spiral_1_val = np.loadtxt('data/spiral_val_1.csv', skiprows=1, delimiter=',')\n",
+    "spiral_2_train = np.loadtxt('data/spiral_train_2.csv', skiprows=1, delimiter=',')\n",
+    "spiral_2_val = np.loadtxt('data/spiral_val_2.csv', skiprows=1, delimiter=',')\n",
+    "\n",
+    "spiral_1_train_y = spiral_1_train[:, 2]\n",
+    "spiral_1_val_y = spiral_1_val[:, 2]\n",
+    "spiral_2_train_y = spiral_2_train[:, 2]\n",
+    "spiral_2_val_y = spiral_2_val[:, 2]\n",
+    "\n",
+    "spiral_1_train = spiral_1_train[:, :2]\n",
+    "spiral_1_val = spiral_1_val[:, :2]\n",
+    "spiral_2_train = spiral_2_train[:, :2]\n",
+    "spiral_2_val = spiral_2_val[:, :2]\n",
+    "\n",
+    "print(f'Spiral 1 train {spiral_1_train.shape}, classes {spiral_1_train_y.shape}')\n",
+    "print(f'Spiral 1 validation {spiral_1_val.shape}, classes {spiral_1_val_y.shape}')\n",
+    "print(f'Spiral 2 train {spiral_2_train.shape}, classes {spiral_2_train_y.shape}')\n",
+    "print(f'Spiral 2 validation {spiral_2_val.shape}, classes {spiral_2_val_y.shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Spiral 2 Val')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your plot here\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(12,12))\n",
+    "\n",
+    "axs[0][0].scatter(spiral_1_train[:, 0], spiral_1_train[:, 1], c=spiral_1_train_y)\n",
+    "axs[0][0].set_title(\"Spiral 1 Train\")\n",
+    "axs[0][1].scatter(spiral_1_val[:, 0], spiral_1_val[:, 1], c=spiral_1_val_y)\n",
+    "axs[0][1].set_title(\"Spiral 1 Val\")\n",
+    "\n",
+    "axs[1][0].scatter(spiral_2_train[:, 0], spiral_2_train[:, 1], c=spiral_2_train_y)\n",
+    "axs[1][0].set_title(\"Spiral 2 Train\")\n",
+    "axs[1][1].scatter(spiral_2_val[:, 0], spiral_2_val[:, 1], c=spiral_2_val_y)\n",
+    "axs[1][1].set_title(\"Spiral 2 Val\")\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1b) Implement KNN\n",
+    "\n",
+    "Implement the following methods in `knn.py`. Test relevant methods using the test code below.\n",
+    "\n",
+    "- Constructor\n",
+    "- `train(data, y)`: Train the KNN classifier on the data `data`, where training samples have corresponding class labels in `y`.\n",
+    "- `predict(data, k)`: Use the trained KNN classifier to predict the class label of each test sample in `data`. Determine class by voting: find the closest `k` training exemplars (training samples) and the class is the majority vote of the classes of these training exemplars.\n",
+    "- `accuracy(y, y_pred)`: Compute percent correct given true data labels `y` and algorithm predicted labels `y_pred`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from knn import KNN"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test: Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy is 0.06 and should be 0.06.\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "test_y = np.random.randint(low=0, high=11, size=(50,))\n",
+    "test_y_pred = np.random.randint(low=0, high=11, size=(50,))\n",
+    "\n",
+    "classifier = KNN(num_classes=0)\n",
+    "acc = classifier.accuracy(test_y, test_y_pred)\n",
+    "print(f'Test accuracy is {acc} and should be 0.06.')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test: 1-KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your accuracy with K=1 is 1.0 and should be 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classes = 4\n",
+    "classifier = KNN(num_classes=n_classes)\n",
+    "classifier.train(spiral_1_train, spiral_1_train_y)\n",
+    "\n",
+    "k = 1\n",
+    "spiral_1_y_pred = classifier.predict(spiral_1_train, k)\n",
+    "acc = classifier.accuracy(y=spiral_1_train_y, y_pred=spiral_1_y_pred)\n",
+    "print(f'Your accuracy with K=1 is {acc} and should be 1.0')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 1:** Explain why in the above test, the accuracy must be 100%."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer 1:** \n",
+    "\n",
+    "The data we used for testing, spiral_1_train, is also the same one that we are doing predictions on. Since KNN relies on memorization, this means it has already seen all the data points and their corresponding classes and so it can recall the classes with perfect accuracy."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test 2-KNN\n",
+    "\n",
+    "*Note: The below test code assumes that you resolve voting ties with the class that has a lower index. There is a numpy function that you may feel inclined to use (or not!) that handles this automatically.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your accuracy with K=2 is 0.88 and should be 0.88\n",
+      "The mismatches between your predicted class of validation samples with indices 750-900 and the expected values are\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0]\n",
+      "Seeing all 0s means everything seems to be working great!\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classes = 4\n",
+    "classifier = KNN(num_classes=n_classes)\n",
+    "classifier.train(spiral_1_train, spiral_1_train_y)\n",
+    "\n",
+    "k = 2\n",
+    "spiral_1_y_pred = classifier.predict(spiral_1_val, k)\n",
+    "acc = classifier.accuracy(y=spiral_1_val_y, y_pred=spiral_1_y_pred)\n",
+    "print(f'Your accuracy with K=2 is {acc:.2f} and should be 0.88')\n",
+    "\n",
+    "true_test_y = np.array([2., 2., 2., 2., 2., 3., 2., 2., 3., 2., 2., 1., 2., 2., 2., 2., 2.,\n",
+    "       2., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 3., 2.,\n",
+    "       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
+    "       2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 2., 2., 2., 2., 3., 3., 2.,\n",
+    "       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 3., 2., 3., 2., 2.,\n",
+    "       2., 2., 2., 2., 3., 2., 2., 2., 2., 2., 1., 2., 2., 2., 2., 2., 2.,\n",
+    "       2., 2., 2., 3., 3., 2., 3., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
+    "       2., 2., 2., 2., 2., 1., 3., 2., 2., 2., 3., 3., 2., 2., 2., 2., 2.,\n",
+    "       2., 2., 2., 2., 3., 2., 2., 2., 2., 2., 2., 2., 2., 2.])\n",
+    "\n",
+    "print(f'The mismatches between your predicted class of validation samples with indices 750-900 and the expected values are\\n{np.where(true_test_y != spiral_1_y_pred[750:900], 1, 0)}')\n",
+    "print('Seeing all 0s means everything seems to be working great!')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1c) Find the best `k`\n",
+    "\n",
+    "- Below, \"script\" your `predict` method on both spiral datasets 1 and 2. Compute the accuracy on the respective test sets with many different values of `k`.\n",
+    "- Create two well-labeled plots, one for each spiral dataset, showing the accuracy for many different `k` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accuracies = []\n",
+    "ks = np.arange(1,20)\n",
+    "\n",
+    "for k in range(1,20):\n",
+    "    classifier.train(spiral_1_train, spiral_1_train_y)\n",
+    "    spiral_1_y_pred = classifier.predict(spiral_1_val, k)\n",
+    "    acc = classifier.accuracy(y=spiral_1_val_y, y_pred=spiral_1_y_pred)\n",
+    "    accuracies.append(acc)\n",
+    "\n",
+    "print(\"Best\", np.argmax(accuracies) + 1)\n",
+    "\n",
+    "plt.plot(ks, accuracies)\n",
+    "plt.xlabel(\"k\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.title(\"Spiral 1: k vs accuracy\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best 11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accuracies = []\n",
+    "ks = np.arange(1,20)\n",
+    "\n",
+    "for k in range(1,20):\n",
+    "    classifier.train(spiral_2_train, spiral_2_train_y)\n",
+    "    spiral_2_y_pred = classifier.predict(spiral_2_val, k)\n",
+    "    acc = classifier.accuracy(y=spiral_2_val_y, y_pred=spiral_2_y_pred)\n",
+    "    accuracies.append(acc)\n",
+    "\n",
+    "print(\"Best\", np.argmax(accuracies) + 1)\n",
+    "plt.plot(ks, accuracies)\n",
+    "plt.xlabel(\"k\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.title(\"Spiral 2 k: vs accuracy\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the `k` that results in the highest accuracy on each spiral dataset?\n",
+    "\n",
+    "**Question 3:** Give at least one \"good\" reason why the accuracies are so different across the datasets. (*Hint: look at the data*)\n",
+    "\n",
+    "**Question 4:** Give at least one \"good\" reason why the best `k` values are so different across the datasets.\n",
+    "\n",
+    "**Question 5:** Is it a good idea to always set `k` to one of these values when working with another dataset?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer 2:**\n",
+    "\n",
+    "For spiral 1, the best accuracy was when k = 1. For spiral 2, the best accuracy was when k = 11. \n",
+    "\n",
+    "**Answer 3:** \n",
+    "\n",
+    "The accuracies are so different across the dataset because of how the data is aligned. For spiral 1, the datapoints in the validation dataset look almost identical to the one in the training except that it is more sparse. So, we get a lot higher accuracy because the validation data is closer to the training and thus easier to predict on. For spiral 2, we see that although the test data looks like spiral 1, the validation data looks a lot different and has more scattering (ie. it doesn't match up with the training as well) as well as having more of the classes mixed together making it harder to preidct and lowering accuracy. \n",
+    "\n",
+    "**Answer 4:** \n",
+    "\n",
+    "Similar to answer 3, the best k values are so different because of the differences in the data. In spiral 1, the val data is not changed much from the train data. In spiral 2, the val data is a lot different. This means that with a k value of 1 sprial 1 does very well because picking only the nearest neighbor probably works enough to figure out what class it is. On the other hand, spiral 2 requires a best k of 11 because it is offset a lot from training data so requires more \"votes\" fro the surrounding classes to get a more accurate preidction. \n",
+    "\n",
+    "**Answer 5:** \n",
+    "\n",
+    "No, the optimal number of k will vary from dataset to dataset. It would be better to run the same script and look at the plot that it produces to find a personalized best k. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1d) Visualize class boundaries\n",
+    "\n",
+    "- Implement `plot_predictions` in `knn.py` to visualize how different regions of the (2D) dataspace would be classified. In this visualization, use four discrete colors to represent each of the classes. For example, if KNN would classify (x, y) = (10, 10) to spiral 2, you would color that region blue (for example). You will repeat this for lots of different regularly spaced x,y points to get a better picture of the regions that would be predicted to belong to different classes.\n",
+    "- For spiral dataset 1 and 2, plot the class boundaries for the k best value determined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classifier = KNN(4)\n",
+    "classifier.train(spiral_1_train, spiral_1_train_y)\n",
+    "classifier.plot_predictions(2, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classifier = KNN(4)\n",
+    "classifier.train(spiral_2_train, spiral_2_train_y)\n",
+    "classifier.plot_predictions(2, 200)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 6:** Why could visualizing the class boundaries be useful?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer 6:** \n",
+    "\n",
+    "Visualizing class boundaries could be useful because then you can kind of see the shape of what your KNN will return. For example, if you only gave it validation data to predict then you would only see the points where the validation data was. But with this visualization, you see the assignemnts for any possible point in the data. It gives you a better idea of what to expect from your classifier. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2: Spam email preprocessing pipeline\n",
+    "\n",
+    "Before you can build a spam email filter, you need to transform the email data into a suitable format so that KNN or other supervised learning algorithms can process them (this is called **preprocessing**).\n",
+    "\n",
+    "In this project, you will work with the **Enron email dataset**, a large datset consisting of ~34,000 emails. Enron is an energy company that famously went bankrupt in the early 2000s after committing massive accounting fraud (more info: https://en.wikipedia.org/wiki/Enron). The US government seized company emails during their investigation and they were released to the public much later and nowadays is a commonly used datset in machine learning. \n",
+    "\n",
+    "Your eventual goal will be to train a supervised learning algorithm on some of the emails and predict whether the remaining ones are spam or not.\n",
+    "\n",
+    "But first...onto the preprocessing!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overall preprocessing strategy\n",
+    "\n",
+    "We need to turn each email's text into something an algorithm can process (**features**). We will use a simple type of feature: **bag of words counts**. That is, we will reduce an email into a vector of how many times words appeared in it.\n",
+    "\n",
+    "*Problem:* There are too many words across all the emails. Processing the counts in each email would take too long. For example, there are more than 40,000 words across all the emails. If we were trying to predict whether 1,000 emails are spam or not, we would need to build a `1000 x 40000` matrix (count each of the 40,000 words in each of the 1,000 emails), which would take a very long time to process by the supervised learning algorithm. \n",
+    "\n",
+    "A work-around that works quite well is to restrict ourselves to the most frequent $W$ words in the email dataset. You can experiment with how many words to include (e.g. as an extension), but for concreteness we will set this $W=200$ in the core project. In the above example, we can then process `1000 x 200` matrix much more quickly."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2a) Determine email word frequency\n",
+    "\n",
+    "The large size of the enron email datset makes the debugging process cumbersome. In situations like this, it is common to work with a **development dataset** — a mini version of the full dataset that is much faster to work with. The enron dev datset has 2 ham emails and 3 spam emails. \n",
+    "\n",
+    "- Download and extract the **Enron dev** emails. You should see a base `enron` folder, with `spam` and `ham` subfolders (these are the 2 classes), and documents in each with the raw email text. There should be 2 files in the ham folder and 3 files in the spam folder.\n",
+    "- In `email_preprocessor.py` implement `count_words(email_path)` to build up a python dictionary of all the words in the dataset (keys) and their associated counts (values).\n",
+    "- Write `find_top_words(word_freq)` to parse the dictionary and determine the top $W$ words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import email_preprocessor as epp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test `count_words` and `find_top_words`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "word_freq, num_emails = epp.count_words(email_path='data/enron_dev/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You found 5 emails in the datset. You should have found 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'You found {num_emails} emails in the datset. You should have found 5.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You found 19/19 words.\n",
+      "Your top 2 words are\n",
+      "['subject', 'you']\n",
+      "and they should be\n",
+      "['subject', 'you']\n",
+      "The counts of all the words are\n",
+      "[5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
+      "and they should be\n",
+      "[5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
+      "The 19 words should are\n",
+      "['subject', 'you', 'get', 'that', 'new', 'car', 'now', 'can', 'be', 'smart', 'love', 'ecards', 'christmas', 'tree', 'farm', 'pictures', 're', 'rankings', 'thank']\n",
+      " and they should be \n",
+      "['subject', 'you', 'get', 'that', 'new', 'car', 'now', 'can', 'be', 'smart', 'love', 'ecards', 'christmas', 'tree', 'farm', 'pictures', 're', 'rankings', 'thank']\n",
+      "with the last 17 words in any order (because their counts are tied)\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_words, top_counts = epp.find_top_words(word_freq)\n",
+    "print(f\"You found {len(top_words)}/19 words.\")\n",
+    "print(f\"Your top 2 words are\\n{top_words[:2]}\\nand they should be\\n['subject', 'you']\")\n",
+    "print(f\"The counts of all the words are\\n{top_counts}\\nand they should be\\n[5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\")\n",
+    "print(f\"The 19 words should are\\n{top_words}\\n and they should be \\n['subject', 'you', 'get', 'that', 'new', 'car', 'now', 'can', 'be', 'smart', 'love', 'ecards', 'christmas', 'tree', 'farm', 'pictures', 're', 'rankings', 'thank']\\nwith the last 17 words in any order (because their counts are tied)\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2b) Make feature vectors based only on the top word counts\n",
+    "\n",
+    "- Implement `make_feature_vectors`: Go back through the email folder structure and parse each email again. Now only count the frequency of words that are in the top $W$ word list. Keep track of whether each of these feature vectors are associated with a spam or not spam email."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hard_code_words = ['subject', 'you', 'get', 'that', 'new', 'car', 'now', 'can', 'be', 'smart', 'love', 'ecards', 'christmas', 'tree', 'farm', 'pictures', 're', 'rankings', 'thank']\n",
+    "features, y = epp.make_feature_vectors(hard_code_words, num_emails, email_path='data/enron_dev/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your matrix of features has shape:\n",
+      "(5, 19)\n",
+      "and it should be\n",
+      "(5, 19).\n",
+      "Your class label vector has shape:\n",
+      "(5,)\n",
+      "and it should be\n",
+      "(5,).\n",
+      "Make sure your features have 0's and 1's in every row\n",
+      "[[1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0.]\n",
+      " [1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1.]]\n",
+      "\n",
+      "Below, one number should be 3, the other should be 2.\n",
+      "Number of emails of class 0: 3\n",
+      "Number of emails of class 1: 2\n",
+      "\n",
+      "Your vector for 2958.2004-11-03.GP.spam.txt matches expected counts?\n",
+      "True\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "firstSpamWordCounts = np.array([1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
+    "\n",
+    "\n",
+    "print(f'Your matrix of features has shape:\\n{features.shape}\\nand it should be\\n(5, 19).')\n",
+    "print(f'Your class label vector has shape:\\n{y.shape}\\nand it should be\\n(5,).')\n",
+    "print(f\"Make sure your features have 0's and 1's in every row\")\n",
+    "print(features)\n",
+    "print('\\nBelow, one number should be 3, the other should be 2.')\n",
+    "print(f'Number of emails of class 0: {np.sum(y == 0)}\\nNumber of emails of class 1: {np.sum(y == 1)}')\n",
+    "\n",
+    "inds = np.arange(len(features))\n",
+    "test_ind = inds[np.all(firstSpamWordCounts == features, axis=1)]\n",
+    "print(f'\\nYour vector for 2958.2004-11-03.GP.spam.txt matches expected counts?\\n{len(test_ind) == 1}\\n')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2c) Make train and test splits of the dataset\n",
+    "\n",
+    "Your matrix of features is for the entire dataset. We can't train the classifier on all these because then we won't have any emails left over to see how well your model's ability to discriminate spam/ham email generalizes to emails not seen during training!\n",
+    "\n",
+    "Implement `make_train_test_sets` to divide the email features into a 80/20 train/test split (80% of data used to train the supervised learning model, 20% we withhold and use for testing / prediction)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "x_train, y_train, inds_train, x_test, y_test, inds_test = epp.make_train_test_sets(features, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes for train/test splits:\n",
+      "Train (4, 19), classes (4,)\n",
+      "Test (1, 19), classes (1,)\n",
+      "\n",
+      "They should be:\n",
+      "Train (4, 19), classes (4,)\n",
+      "Test (1, 19), classes (1,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Shapes for train/test splits:')\n",
+    "print(f'Train {x_train.shape}, classes {y_train.shape}')\n",
+    "print(f'Test {x_test.shape}, classes {y_test.shape}')\n",
+    "print('\\nThey should be:\\nTrain (4, 19), classes (4,)\\nTest (1, 19), classes (1,)')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000..dd0c5d3
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,137 @@
+'''knn.py
+K-Nearest Neighbors algorithm for classification
+'''
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from palettable import cartocolors
+
+
+class KNN:
+    '''K-Nearest Neighbors supervised learning algorithm'''
+    def __init__(self, num_classes):
+        '''KNN constructor
+        '''
+        self.exemplars = None
+        self.classes = None
+
+        self.num_classes = num_classes
+
+    def train(self, data, y):
+        '''Train the KNN classifier on the data `data`, where training samples have corresponding
+        class labels in `y`.
+
+        Parameters:
+        -----------
+        data: ndarray. shape=(num_train_samps, num_features). Data to learn / train on.
+        y: ndarray. shape=(num_train_samps,). Corresponding class of each data sample.
+        '''
+        self.exemplars = data
+        self.classes = y
+
+    def predict(self, data, k):
+        '''Use the trained KNN classifier to predict the class label of each test sample in `data`.
+
+        Parameters:
+        -----------
+        data: ndarray. shape=(num_test_samps, num_features). Data to predict the class of
+        k: int. Determines the neighborhood size of training points around each test sample used to
+            make class predictions.
+
+        Returns:
+        -----------
+        ndarray of nonnegative ints. shape=(num_test_samps,). Predicted class of each test data
+        sample.
+
+        '''
+
+        predicted_classes = []
+        
+        for sample in range(data.shape[0]):
+            dist = np.sqrt(np.sum(np.square(self.exemplars - data[sample]), axis = 1))
+            # print(dist.shape)
+            closest = np.argpartition(dist, k)[:k]
+            classes, counts = np.unique(self.classes[closest], return_counts = True)
+            predicted_classes.append(classes[np.argmax(counts)])
+        
+        return np.array(predicted_classes)
+
+    def accuracy(self, y, y_pred):
+        '''Computes accuracy based on percent correct: Proportion of predicted class labels `y_pred`
+        that match the true values `y`.
+
+        Parameters:
+        -----------
+        y: ndarray. shape=(num_data_sams. Ground-truth
+        y_pred: ndarray. shape=(num_data_sams,)
+            Predicted class labels by the model for each data sample
+
+        Returns:
+        -----------
+        float. Proportion correct classification.
+
+        '''
+        N = y.shape[0]
+
+        correct = np.sum(np.where(y == y_pred, 1, 0))
+
+        return correct/N 
+
+    def plot_predictions(self, k, n_sample_pts):
+        '''Paints the data space in colors corresponding to which class the classifier would
+         hypothetically assign to data samples appearing in each region.
+
+        Parameters:
+        -----------
+        k: int. Determines the neighborhood size of training points around each test sample used to
+            make class predictions.
+        n_sample_pts: int.
+        '''
+        
+        color = ListedColormap(cartocolors.qualitative.Safe_4.mpl_colors)
+
+        vector = np.linspace(-40, 40, n_sample_pts)
+        x, y = np.meshgrid(vector, vector)
+        
+        data = np.column_stack((x.flatten(), y.flatten()))
+        data = np.reshape(data, (n_sample_pts * n_sample_pts, self.exemplars.shape[1]))
+
+        y_pred = self.predict(data, k)
+        
+        y_pred = np.reshape(y_pred, (n_sample_pts, n_sample_pts))
+
+        colors = plt.pcolormesh(x, y, y_pred, cmap=color)
+
+        plt.colorbar(colors)
+        plt.title("Plot of Predictions")
+        plt.xlabel("X")
+        plt.ylabel("Y")
+
+
+    def confusion_matrix(self, y, y_pred):
+        '''Create a confusion matrix based on the ground truth class labels (`y`) and those predicted
+        by the classifier (`y_pred`).
+
+        Parameters:
+        -----------
+        y: ndarray. shape=(num_data_samps,)
+            Ground-truth, known class labels for each data sample
+        y_pred: ndarray. shape=(num_data_samps,)
+            Predicted class labels by the model for each data sample
+
+        Returns:
+        -----------
+        ndarray. shape=(num_classes, num_classes).
+            Confusion matrix
+        '''
+        
+        matrix = np.zeros((self.num_classes, self.num_classes))
+
+        for i in range(self.num_classes):
+            for j in range(self.num_classes):
+                act_match = np.where(y == i, 1, 0)
+                pred_match = np.where(y_pred == j, 1, 0)
+                matches = np.logical_and(act_match, pred_match)
+                matrix[i, j] = np.sum(np.where(matches == True, 1, 0))
+
+        return matrix
diff --git a/naive_bayes.ipynb b/naive_bayes.ipynb
new file mode 100644
index 0000000..680d8cc
--- /dev/null
+++ b/naive_bayes.ipynb
@@ -0,0 +1,1424 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Narit Trikasemsak**\n",
+    "\n",
+    "Spring 2023\n",
+    "\n",
+    "CS 251/2: Data Analysis and Visualization\n",
+    "\n",
+    "Project 6: Supervised learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "plt.style.use(['seaborn-v0_8-colorblind', 'seaborn-v0_8-darkgrid'])\n",
+    "plt.rcParams.update({'font.size': 20})\n",
+    "\n",
+    "np.set_printoptions(suppress=True, precision=5)\n",
+    "\n",
+    "# Automatically reload external modules\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 3: Preprocess full spam email dataset \n",
+    "\n",
+    "Before you build a Naive Bayes spam email classifier, run the full spam email dataset through your preprocessing code.\n",
+    "\n",
+    "Download and extract the full **Enron** emails (*zip file should be ~29MB large*). You should see a base `enron` folder, with `spam` and `ham` subfolders when you extract the zip file (these are the 2 classes).\n",
+    "\n",
+    "Run the test code below to check everything over."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3a) Preprocess dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import email_preprocessor as epp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test `count_words` and `find_top_words`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "word_freq, num_emails = epp.count_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You found 32625 emails in the datset. You should have found 32625.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'You found {num_emails} emails in the datset. You should have found 32625.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your top 5 words are\n",
+      "['the', 'to', 'and', 'of', 'a']\n",
+      "and they should be\n",
+      "['the', 'to', 'and', 'of', 'a']\n",
+      "The associated counts are\n",
+      "[277459, 203659, 148873, 139578, 111796]\n",
+      "and they should be\n",
+      "[277459, 203659, 148873, 139578, 111796]\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_words, top_counts = epp.find_top_words(word_freq)\n",
+    "print(f\"Your top 5 words are\\n{top_words[:5]}\\nand they should be\\n['the', 'to', 'and', 'of', 'a']\")\n",
+    "print(f\"The associated counts are\\n{top_counts[:5]}\\nand they should be\\n[277459, 203659, 148873, 139578, 111796]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3b) Make train and test splits of the dataset\n",
+    "\n",
+    "Here we divide the email features into a 80/20 train/test split (80% of data used to train the supervised learning model, 20% we withhold and use for testing / prediction)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features, y = epp.make_feature_vectors(top_words, num_emails)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "x_train, y_train, inds_train, x_test, y_test, inds_test = epp.make_train_test_sets(features, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes for train/test splits:\n",
+      "Train (26100, 200), classes (26100,)\n",
+      "Test (6525, 200), classes (6525,)\n",
+      "\n",
+      "They should be:\n",
+      "Train (26100, 200), classes (26100,)\n",
+      "Test (6525, 200), classes (6525,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Shapes for train/test splits:')\n",
+    "print(f'Train {x_train.shape}, classes {y_train.shape}')\n",
+    "print(f'Test {x_test.shape}, classes {y_test.shape}')\n",
+    "print('\\nThey should be:\\nTrain (26100, 200), classes (26100,)\\nTest (6525, 200), classes (6525,)')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3c) Save data in binary format\n",
+    "\n",
+    "It adds a lot of overhead to have to run through your raw email -> train/test feature split every time you wanted to work on your project! In this step, you will export the data in memory to disk in a binary format. That way, you can quickly load all the data back into memory (directly in ndarray format) whenever you want to work with it again. No need to parse from text files!\n",
+    "\n",
+    "Running the following cell uses numpy's `save` function to make six files in `.npy` format (e.g. `email_train_x.npy`, `email_train_y.npy`, `email_train_inds.npy`, `email_test_x.npy`, `email_test_y.npy`, `email_test_inds.npy`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.save('data/email_train_x.npy', x_train)\n",
+    "np.save('data/email_train_y.npy', y_train)\n",
+    "np.save('data/email_train_inds.npy', inds_train)\n",
+    "np.save('data/email_test_x.npy', x_test)\n",
+    "np.save('data/email_test_y.npy', y_test)\n",
+    "np.save('data/email_test_inds.npy', inds_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 4: Naive Bayes Classifier\n",
+    "\n",
+    "After finishing your email preprocessing pipeline, implement the one other supervised learning algorithm we we will use to classify email, **Naive Bayes**."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4a) Implement Naive Bayes\n",
+    "\n",
+    "In `naive_bayes.py`, implement the following methods:\n",
+    "- Constructor\n",
+    "- get methods\n",
+    "- `train(data, y)`: Train the Naive Bayes classifier so that it records the \"statistics\" of the training set: class priors (i.e. how likely an email is in the training set to be spam or ham?) and the class likelihoods (the probability of a word appearing in each class — spam or ham).\n",
+    "- `predict(data)`: Combine the class likelihoods and priors to compute the posterior distribution. The predicted class for a test sample is the class that yields the highest posterior probability.\n",
+    "- `accuracy(y, y_pred)`: The usual definition :)\n",
+    "\n",
+    "\n",
+    "#### Bayes rule ingredients: Priors and likelihood (`train`)\n",
+    "\n",
+    "To compute class predictions (probability that a test example belong to either spam or ham classes), we need to evaluate **Bayes Rule**. This means computing the priors and likelihoods based on the training data.\n",
+    "\n",
+    "**Prior:** $$P_c = \\frac{N_c}{N}$$ where $P_c$ is the prior for class $c$ (spam or ham), $N_c$ is the number of training samples that belong to class $c$ and $N$ is the total number of training samples.\n",
+    "\n",
+    "**Likelihood:** $$L_{c,w} = \\frac{N_{c,w} + 1}{N_{c} + M}$$ where\n",
+    "- $L_{c,w}$ is the likelihood that word $w$ belongs to class $c$ (*i.e. what we are solving for*)\n",
+    "- $N_{c,w}$ is the total count of **word $w$** in emails that are only in class $c$ (*either spam or ham*)\n",
+    "- $N_{c}$ is the total number of **all words** that appear in emails of the class $c$ (*total number of words in all spam emails or total number of words in all ham emails*)\n",
+    "- $M$ is the number of features (*number of top words*).\n",
+    "\n",
+    "#### Bayes rule ingredients: Posterior (`predict`)\n",
+    "\n",
+    "To make predictions, we now combine the prior and likelihood to get the posterior:\n",
+    "\n",
+    "**Log Posterior:** $$Log(\\text{Post}_{i, c}) = Log(P_c) + \\sum_{j \\in J_i}x_{i,j}Log(L_{c,j})$$\n",
+    "\n",
+    " where\n",
+    "- $\\text{Post}_{i,c}$ is the posterior for class $c$ for test sample $i$(*i.e. evidence that email $i$ is spam or ham*). We solve for its logarithm.\n",
+    "- $Log(P_c)$ is the logarithm of the prior for class $c$.\n",
+    "- $x_{i,j}$ is the number of times the jth word appears in the ith email.\n",
+    "- $Log(L_{c,j})$: is the log-likelihood of the jth word in class $c$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from naive_bayes import NaiveBayes"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test `train`\n",
+    "\n",
+    "###### Class priors and likelihoods\n",
+    "\n",
+    "The following test should be used only if storing the class priors and likelihoods directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your class priors are: [0.28 0.22 0.32 0.18]\n",
+      "and should be          [0.28 0.22 0.32 0.18].\n",
+      "Your class likelihoods shape is (4, 6) and should be (4, 6).\n",
+      "Your likelihoods are:\n",
+      "[[0.15997 0.15091 0.2079  0.19106 0.14184 0.14832]\n",
+      " [0.11859 0.16821 0.17914 0.16905 0.18082 0.18419]\n",
+      " [0.16884 0.17318 0.14495 0.14332 0.18784 0.18187]\n",
+      " [0.16126 0.17011 0.15831 0.13963 0.18977 0.18092]]\n",
+      "and should be\n",
+      "[[0.15997 0.15091 0.2079  0.19106 0.14184 0.14832]\n",
+      " [0.11859 0.16821 0.17914 0.16905 0.18082 0.18419]\n",
+      " [0.16884 0.17318 0.14495 0.14332 0.18784 0.18187]\n",
+      " [0.16126 0.17011 0.15831 0.13963 0.18977 0.18092]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_test_classes = 4\n",
+    "np.random.seed(0)\n",
+    "data_test = np.random.randint(low=0, high=20, size=(100, 6))\n",
+    "y_test = np.random.randint(low=0, high=num_test_classes, size=(100,))\n",
+    "\n",
+    "nbc = NaiveBayes(num_classes=num_test_classes)\n",
+    "nbc.train(data_test, y_test)\n",
+    "\n",
+    "print(f'Your class priors are: {nbc.get_priors()}\\nand should be          [0.28 0.22 0.32 0.18].')\n",
+    "print(f'Your class likelihoods shape is {nbc.get_likelihoods().shape} and should be (4, 6).')\n",
+    "print(f'Your likelihoods are:\\n{nbc.get_likelihoods()}')\n",
+    "\n",
+    "print(f'and should be')\n",
+    "print('''[[0.15997 0.15091 0.2079  0.19106 0.14184 0.14832]\n",
+    " [0.11859 0.16821 0.17914 0.16905 0.18082 0.18419]\n",
+    " [0.16884 0.17318 0.14495 0.14332 0.18784 0.18187]\n",
+    " [0.16126 0.17011 0.15831 0.13963 0.18977 0.18092]]''')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Log of class priors and likelihoods\n",
+    "\n",
+    "This test should be used only if storing the log of the class priors and likelihoods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_test_classes = 4\n",
+    "np.random.seed(0)\n",
+    "data_test = np.random.randint(low=0, high=20, size=(100, 6))\n",
+    "y_test = np.random.randint(low=0, high=num_test_classes, size=(100,))\n",
+    "\n",
+    "nbc = NaiveBayes(num_classes=num_test_classes)\n",
+    "nbc.train(data_test, y_test)\n",
+    "\n",
+    "print(f'Your log class priors are: {nbc.get_priors()}\\nand should be              [-1.27297 -1.51413 -1.13943 -1.7148 ].')\n",
+    "print(f'Your log class likelihoods shape is {nbc.get_likelihoods().shape} and should be (4, 6).')\n",
+    "print(f'Your log likelihoods are:\\n{nbc.get_likelihoods()}')\n",
+    "\n",
+    "\n",
+    "print(f'and should be')\n",
+    "print('''[[-1.83274 -1.89109 -1.57069 -1.65516 -1.95306 -1.90841]\n",
+    " [-2.13211 -1.78255 -1.71958 -1.77756 -1.71023 -1.6918 ]\n",
+    " [-1.77881 -1.75342 -1.93136 -1.94266 -1.67217 -1.70448]\n",
+    " [-1.82475 -1.77132 -1.84321 -1.96879 -1.66192 -1.70968]]''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test `predict`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your predicted classes are\n",
+      "[3 0 3 1 0 1 1 3 0 3 0 2 0 2 1]\n",
+      "and should be\n",
+      "[3 0 3 1 0 1 1 3 0 3 0 2 0 2 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_test_classes = 4\n",
+    "np.random.seed(0)\n",
+    "data_train = np.random.randint(low=0, high=num_test_classes, size=(100, 10))\n",
+    "data_test = np.random.randint(low=0, high=num_test_classes, size=(15, 10))\n",
+    "y_test = np.random.randint(low=0, high=num_test_classes, size=(100,))\n",
+    "\n",
+    "nbc = NaiveBayes(num_classes=num_test_classes)\n",
+    "nbc.train(data_train, y_test)\n",
+    "test_y_pred = nbc.predict(data_test)\n",
+    "\n",
+    "print(f'Your predicted classes are\\n{test_y_pred}\\nand should be\\n[3 0 3 1 0 1 1 3 0 3 0 2 0 2 1]')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4b) Spam filtering\n",
+    "\n",
+    "Let's start classifying spam email using the Naive Bayes classifier. The following code uses `np.load` to load in the train/test split that you created last week.\n",
+    "- Use your Naive Bayes classifier on the Enron email dataset!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 7:** Print out the accuracy that you get on the test set with Naive Bayes. It should be roughly 89%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import email_preprocessor as ep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = np.load('data/email_train_x.npy')\n",
+    "y_train = np.load('data/email_train_y.npy')\n",
+    "inds_train = np.load('data/email_train_inds.npy')\n",
+    "x_test = np.load('data/email_test_x.npy')\n",
+    "y_test = np.load('data/email_test_y.npy')\n",
+    "inds_test = np.load('data/email_test_inds.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is 89.24137931034483 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "enron_nbc = NaiveBayes(num_classes=2)\n",
+    "enron_nbc.train(x_train, y_train)\n",
+    "\n",
+    "enron_pred = enron_nbc.predict(x_test)\n",
+    "enron_acc = enron_nbc.accuracy(y_test, enron_pred)\n",
+    "print(\"Accuracy is\", enron_acc, \"%\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get an accuracy of roughly 89% as desired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4c) Confusion matrix\n",
+    "\n",
+    "To get a better sense of the errors that the Naive Bayes classifer makes, you will create a confusion matrix. \n",
+    "\n",
+    "- Implement `confusion_matrix` in `naive_bayes.py`.\n",
+    "- Print out a confusion matrix of the spam classification results.\n",
+    "\n",
+    "**Debugging guidelines**:\n",
+    "1. The sum of all numbers in your 2x2 confusion matrix should equal the number of test samples (6525).\n",
+    "2. The sum of your spam row should equal the number of spam samples in the test set (3193)\n",
+    "3. The sum of your ham row should equal the number of spam samples in the test set (3332)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3043.  150.]\n",
+      " [ 552. 2780.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix = enron_nbc.confusion_matrix(y_test, enron_pred)\n",
+    "print(matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 8:** Interpret the confusion matrix, using the convention that positive detection means spam (*e.g. a false positive means classifying a ham email as spam*). What types of errors are made more frequently by the classifier? What does this mean (*i.e. X (spam/ham) is more likely to be classified than Y (spam/ham) than the other way around*)?\n",
+    "\n",
+    "**Reminder:** Look back and make sure you are clear on which class indices correspond to spam/ham."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer 8:**\n",
+    "\n",
+    "I did not do my class indices alphabetically, so spam is 0 and ham is 1. Then, that means that the top right corner is true spam vs predicted ham, or false negatives. The bottom left corner is false positive, true ham as predicted spam. \n",
+    "\n",
+    "We see that out of 3193 true spam, we classified 150 as ham = 4.69% false negative rate. \n",
+    "\n",
+    "At the same time, out of 3332 true ham, we classified 552 as spam = 16.57% false positive rate. \n",
+    "\n",
+    "Looking at the confusion matrix, we see that there are more false positives than negatives, ie. ham is more likely to be classified as spam than the other way around. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 5: Comparison with KNN\n",
+    "\n",
+    "\n",
+    "- Run a similar analysis to what you did with Naive Bayes above. When computing accuracy on the test set, you may want to reduce the size of the test set (e.g. to the first 500 emails in the test set).\n",
+    "- Copy-paste your `confusion_matrix` method into `knn.py` so that you can run the same analysis on a KNN classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from knn import KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enron_knn = KNN(2)\n",
+    "enron_knn.train(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNN has an accuracy of 91.14176245210727 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "knn_pred = enron_knn.predict(x_test, 2)\n",
+    "knn_acc = enron_knn.accuracy(y_test, knn_pred)\n",
+    "print(\"KNN has an accuracy of\", knn_acc*100, \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3117.   76.]\n",
+      " [ 502. 2830.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix = enron_knn.confusion_matrix(y_test, knn_pred)\n",
+    "print(matrix)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Question 9:** What accuracy did you get on the test set (potentially reduced in size)?\n",
+    "\n",
+    "**Question 10:** How does the confusion matrix compare to that obtained by Naive Bayes (*If you reduced the test set size, keep that in mind*)?\n",
+    "\n",
+    "**Question 11:** Briefly describe at least one pro/con of KNN compared to Naive Bayes on this dataset.\n",
+    "\n",
+    "**Question 12:** When potentially reducing the size of the test set here, why is it important that we shuffled our train and test set?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer 9:** \n",
+    "\n",
+    "I did not reduce test set size and got an accuracy of about 91.14%. \n",
+    "\n",
+    "**Answer 10:** \n",
+    "\n",
+    "From initial impressions it seems like the confusion matrix shows slighly less false positives and false negatives compared to Naive Bayes. \n",
+    "\n",
+    "We got a false negative rate of about 2.38%, which is almost halved compared the naive bayes. \n",
+    "\n",
+    "We got a false positive rate about 15.06%, which is slightly less than the naive bayes. \n",
+    "\n",
+    "**Answer 11:** \n",
+    "\n",
+    "One con of KNN compared to Naive Bayes on this dataset is that it is much more computationally expensive to classify with KNN. This is shown by how much longer it took to train the KNN (1 minute) vs the Naive Bayes (1.6 seconds).\n",
+    "\n",
+    "One pro of KNN in this situation is higher accuracy as well as the much better false negative and slightly better false positive rate. This could be due to the structure of KNN not assuming that each word is independent of each other, which makes sense given that words are often associated with the ones that come before it. \n",
+    "\n",
+    "**Answer 12:** \n",
+    "\n",
+    "It is important because the shuffling of the train and test set mean that the data, ie. spam and ham emails, are randomly distributed across both sets which means that the two sets are more representative of the population it came from."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0. Classify your own datasets\n",
+    "\n",
+    "- Find datasets that you find interesting and run classification on them using your KNN algorithm (and if applicable, Naive Bayes). Analysis the performance of your classifer."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extension: Wine Quality dataset\n",
+    "\n",
+    "For this extension, I wanted to use the wine qualtiy dataset because it has been something I explored in previous projects, and now with the introduction of classification, finally have the tools to tackle the \"quality\" part. For reference, the wine datast is described as follows from previous projects: \n",
+    "\n",
+    "The wine quality dataset is a large dataset with a number of variables. It looks at red Portuguese \"Vinho Verde\" wine. The variables include physicochemical (inputs) and sensory (the output). They are: \n",
+    "\n",
+    "   1 - fixed acidity\n",
+    "\n",
+    "   2 - volatile acidity\n",
+    "\n",
+    "   3 - citric acid\n",
+    "\n",
+    "   4 - residual sugar\n",
+    "\n",
+    "   5 - chlorides\n",
+    "\n",
+    "   6 - free sulfur dioxide\n",
+    "   \n",
+    "   7 - total sulfur dioxide\n",
+    "\n",
+    "   8 - density\n",
+    "\n",
+    "   9 - pH\n",
+    "\n",
+    "   10 - sulphates\n",
+    "\n",
+    "   11 - alcohol\n",
+    "   \n",
+    "   Output variable (based on sensory data): \n",
+    "   12 - quality (score between 0 and 10)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When I ran PCA on the wine quality in prior projects I found that the variance was most explained by these 5 variables: \n",
+    "\n",
+    "['fixed acidity', 'volatile acidity', 'citric acid', 'chlorides', 'free sulfur dioxide']\n",
+    "\n",
+    "To keep to a rasonable computation time, I will choose only these 5 as my features, and then have quality as the label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"data/winequality-red.csv\")\n",
+    "df = df.drop(0)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1595</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1597</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1598</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.645</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1599 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     fixed acidity volatile acidity citric acid chlorides free sulfur dioxide\n",
+       "1              7.4              0.7           0     0.076                  11\n",
+       "2              7.8             0.88           0     0.098                  25\n",
+       "3              7.8             0.76        0.04     0.092                  15\n",
+       "4             11.2             0.28        0.56     0.075                  17\n",
+       "5              7.4              0.7           0     0.076                  11\n",
+       "...            ...              ...         ...       ...                 ...\n",
+       "1595           6.2              0.6        0.08      0.09                  32\n",
+       "1596           5.9             0.55         0.1     0.062                  39\n",
+       "1597           6.3             0.51        0.13     0.076                  29\n",
+       "1598           5.9            0.645        0.12     0.075                  32\n",
+       "1599             6             0.31        0.47     0.067                  18\n",
+       "\n",
+       "[1599 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_feats = df[['fixed acidity', 'volatile acidity', 'citric acid', 'chlorides', 'free sulfur dioxide']]\n",
+    "wine_labels = df[\"quality\"]\n",
+    "wine_feats"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I do the standard 80/20 test split on it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wine_feats = wine_feats.to_numpy(dtype=float)\n",
+    "wine_labels = wine_labels.to_numpy(dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x train shape (1279, 5)\n",
+      "y train shape (1279,)\n",
+      "x test shape (320, 5)\n",
+      "y test shape (320,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "inds = np.arange(wine_labels.size)\n",
+    "\n",
+    "wine_feats = wine_feats.copy()\n",
+    "y = wine_labels.copy()\n",
+    "\n",
+    "inds = np.arange(y.size)\n",
+    "np.random.shuffle(inds)\n",
+    "wine_feats = wine_feats[inds]\n",
+    "y = y[inds]\n",
+    "\n",
+    "# Your code here:\n",
+    "\n",
+    "train = int(wine_feats.shape[0] * (0.8))\n",
+    "\n",
+    "x_train = wine_feats[:train, :]\n",
+    "y_train = y[:train]\n",
+    "\n",
+    "x_test = wine_feats[train:, :]\n",
+    "y_test = y[train:]\n",
+    "\n",
+    "print(\"x train shape\", x_train.shape)\n",
+    "print(\"y train shape\", y_train.shape)\n",
+    "print(\"x test shape\", x_test.shape)\n",
+    "print(\"y test shape\", y_test.shape)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the labels..\n",
+    "\n",
+    "however, looking at the data, we see that not all of the classes are use. Thus, we take the number of unique ones to narrow it down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1       5\n",
+       "2       5\n",
+       "3       5\n",
+       "4       6\n",
+       "5       5\n",
+       "       ..\n",
+       "1595    5\n",
+       "1596    6\n",
+       "1597    6\n",
+       "1598    5\n",
+       "1599    6\n",
+       "Name: quality, Length: 1599, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['3' '4' '5' '6' '7' '8']\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(wine_labels)\n",
+    "print(np.unique(wine_labels))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I also have to normalize my features. For ease of use, I decided to find use the preprocessing module available from sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "wine_feats = preprocessing.normalize(wine_feats)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we see that we have 6 unique classes. Equipped with these, I then run KNN classification on it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wine_knn = KNN(num_classes=6)\n",
+    "wine_knn.train(x_train, y_train)\n",
+    "y_wine_pred = wine_knn.predict(x_test, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "we get an accuracy of  46.5625 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc = wine_knn.accuracy(y_test, y_wine_pred)\n",
+    "print(\"we get an accuracy of \", acc*100, \"%\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get an accuracy of about 46.56% from this dataset which is pretty good considering there are 6 seperate classes. So, our classifier is performing better than random choice!\n",
+    "\n",
+    "We view the confusion matrix to see the breakdown of predictions vs truth values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  2.],\n",
+       "       [ 0.,  0.,  0.,  1.,  0.,  7.],\n",
+       "       [ 0.,  0.,  0.,  0.,  3., 82.]])"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat = wine_knn.confusion_matrix(y_test, y_wine_pred)\n",
+    "mat"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we run it on Naive Bayes to see the performance comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "we get an accuracy of  42.1875 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "wine_knn = NaiveBayes(num_classes=6)\n",
+    "wine_knn.train(x_train, y_train)\n",
+    "y_wine_pred = wine_knn.predict(x_test)\n",
+    "acc = wine_knn.accuracy(y_test, y_wine_pred)\n",
+    "print(\"we get an accuracy of \", acc, \"%\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get a slightly lower accruacy of 42.19% which might be explained by a similar reason where the Naive Bayes assumes everytihng is independent, however, if we look at our feature names a lot of them are acidity which indicates that they might have some relationship between each other.\n",
+    "\n",
+    "\n",
+    "Overall, this was an interesting experiment to see how our classifier would perform on numeric data with more features. Again, our KNN performs slightly better than the Naive Bayes. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Better text preprocessing\n",
+    "\n",
+    "- If you look at the top words extracted from the email dataset, many of them are common \"stop words\" (e.g. a, the, to, etc.) that do not carry much meaning when it comes to differentiating between spam vs. non-spam email. Improve your preprocessing pipeline by building your top words without stop words. Analyze performance differences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Feature size\n",
+    "\n",
+    "- Explore how the number of selected features for the email dataset influences accuracy and runtime performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Distance metrics\n",
+    "- Compare KNN performance with the $L^2$ and $L^1$ distance metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. K-Fold Cross-Validation\n",
+    "\n",
+    "- Research this technique and apply it to data and your KNN and/or Naive Bayes classifiers."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extension: k-fold cross-validation\n",
+    "\n",
+    "For this extension, I wanted to implement k-fold cross validation to better evaluate the performance of the model compared to the test/train split that we were using. I reesarched online and found the procedure to be:\n",
+    "\n",
+    "Shuffle the dataset randomly.\n",
+    "\n",
+    "Split the dataset into k groups\n",
+    "\n",
+    "For each unique group:\n",
+    "\n",
+    "    Take the group as a hold out or test data set\n",
+    "\n",
+    "    Take the remaining groups as a training data set\n",
+    "\n",
+    "    Fit a model on the training set and evaluate it on the test set\n",
+    "\n",
+    "    Retain the evaluation score and discard the model\n",
+    "    \n",
+    "Summarize the skill of the model using the sample of model evaluation scores\n",
+    "\n",
+    "I implemented this in my Naive Bayes classifier for faster run times and evaluate the metrics produced compared to train/test below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(32625, 200)\n",
+      "start 0 end 10875\n",
+      "test (10875, 200)\n",
+      "train (21750, 200)\n",
+      "start 10875 end 21750\n",
+      "test (10875, 200)\n",
+      "train (21750, 200)\n",
+      "start 21750 end 32625\n",
+      "test (10875, 200)\n",
+      "train (21750, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(features.shape)\n",
+    "kfold = NaiveBayes(2)\n",
+    "kfold.kfold(features, y, 3)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code is to demonstrate that my k-fold splits work. Features is all the data vectors, with 32625 samples, and splitting that into 3-folds means that my test is 10875 samples (1 fold) while my training is 21750 samples (the other 2 folds). The start and stop indices also show the sliding window for the kth validation fold.\n",
+    "\n",
+    "I then do the full k-fold cross validation on k = 10 and return the produced accuracies in a list. To evaluate, I will plot the accuracies as well as take the average of the list and compare it to the accuracy of the test/train split from before for Naive Bayes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = NaiveBayes(2)\n",
+    "acc = kfold.kfold(features, y, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(10)\n",
+    "plt.plot(x, acc)\n",
+    "plt.xlabel(\"k-th fold as validation\")\n",
+    "plt.ylabel(\"accuracy\")\n",
+    "plt.title(\"Accuracy of Naive Bayes over kth fold\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the mean accuracy with k-fold cross validation is 89.57694665849172 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg = np.mean(acc)\n",
+    "print(\"the mean accuracy with k-fold cross validation is\", avg, \"%\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a regular test/train split, we got an accruacy of about 89.24%\n",
+    "\n",
+    "With k-fold cross validation, we got a slightly higher average accuracy of about 89.58%\n",
+    "\n",
+    "As we can see, the k-fold cross validation technique actually results in a higher accuracy than with a simple test/train split, which means that the way in which we shuffled the test/train probably in fact lowered the accuracy by a little bit. The k-fold cross-validation gives us a less biased accuracy metric because it cross validates against a number of train/test splits which means that we can actually expect our naive bayes to perform better than we thought. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extension: Follow up to k-fold\n",
+    "\n",
+    "Doing some more research on k-fold cross validation, I saw that there was some debate on the optimal number of k to choose for k-fold cross validation. To explore this further, I decided to find the optimal choice of k for this specific enron data set and see which would give me the best performance. I explore this from k = 2 to k = 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_accs = []\n",
+    "\n",
+    "for k in range(2,10):\n",
+    "    kfold = NaiveBayes(2)\n",
+    "    acc = kfold.kfold(features, y, k)\n",
+    "    avg_accs.append(np.mean(acc))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(2,10)\n",
+    "plt.plot(x, avg_accs)\n",
+    "plt.xlabel(\"choice of k\")\n",
+    "plt.ylabel(\"accuracy\")\n",
+    "plt.title(\"Optimal choice of k for Naive Bayes\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We get the highest accuracy of  89.68888888888888 % at k =  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = np.argmax(avg_accs)\n",
+    "print(\"We get the highest accuracy of \", avg_accs[i], \"%\", \"at k = \", i + 1)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interestingly enough, in this case, we get the best accuracy of around 89.69% at k = 2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Email error analysis\n",
+    "\n",
+    "- Dive deeper into the properties of the emails that were misclassified (FP and/or FN) by Naive Bayes or KNN. What is their word composition? How many words were skipped because they were not in the training set? What could plausibly account for the misclassifications?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Investigate the misclassification errors\n",
+    "\n",
+    "Numbers are nice, but they may not the best for developing your intuition. Sometimes, you want to see what an misclassification *actually looks like* to help you improve your algorithm. Retrieve the actual text of some example emails of false positive and false negative misclassifications to see if helps you understand why the misclassification occurred. Here is an example workflow:\n",
+    "\n",
+    "- Decide on how many FP and FN emails you would like to retrieve. Find the indices of this many false positive and false negative misclassification. Remember to use your `test_inds` array to look up the index of the emails BEFORE shuffling happened.\n",
+    "- Implement the function `retrieve_emails` in `email_preprocessor.py` to return the string of the raw email at the error indices.\n",
+    "- Call your function to print out the emails that produced misclassifications.\n",
+    "\n",
+    "Do the FP and FN emails make sense? Why? Do the emails have properties in common? Can you quantify and interpret them?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Acknowledgments\n",
+    "\n",
+    "https://machinelearningmastery.com/k-fold-cross-validation/ for kfold cross validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/naive_bayes.py b/naive_bayes.py
new file mode 100644
index 0000000..7842224
--- /dev/null
+++ b/naive_bayes.py
@@ -0,0 +1,175 @@
+'''naive_bayes_multinomial.py
+Naive Bayes classifier with Multinomial likelihood for discrete features
+'''
+import numpy as np
+
+
+class NaiveBayes:
+    '''Naive Bayes classifier using Multinomial likeilihoods'''
+    def __init__(self, num_classes):
+        '''Naive Bayes constructor
+        '''
+        self.num_classes = num_classes
+        self.class_priors = None
+        self.class_likelihoods = None
+
+    def get_priors(self):
+        '''Returns the class priors'''
+        return self.class_priors
+
+    def get_likelihoods(self):
+        '''Returns the class likelihoods'''
+        return self.class_likelihoods
+
+    def train(self, data, y):
+        '''Train the Naive Bayes classifier so that it records the "statistics" of the training set
+
+        Parameters:
+        -----------
+        data: ndarray. shape=(num_samps, num_features).
+        y: ndarray. shape=(num_samps,). 
+        '''
+        num_features = data.shape[1]
+        self.class_likelihoods = np.zeros((self.num_classes, num_features))
+        self.class_priors = np.zeros(self.num_classes)
+
+        for i in range(self.num_classes):
+            matches = np.where(y == i, 1, 0)
+            self.class_priors[i] = np.sum(matches)/y.shape[0]
+            for j in range(data.shape[1]):
+                idx = np.where(y == i)
+                class_words = np.sum(data[:, j][idx])
+                total_words = np.sum(data[idx, :])
+                likelihoods = (class_words + 1)/(total_words + data.shape[1])
+                self.class_likelihoods[i, j] = likelihoods
+
+
+    def predict(self, data):
+        '''Combine the class likelihoods and priors to compute the posterior distribution.
+
+        Parameters:
+        -----------
+        data: ndarray. shape=(num_test_samps, num_features).
+
+        Returns:
+        -----------
+        ndarray of nonnegative ints. shape=(num_samps,). Predicted class of each test data sample.
+        '''
+        log_prior = np.log(self.class_priors)
+        log_posterior = log_prior + data @ np.log(self.class_likelihoods).T
+
+        classes = np.argmax(log_posterior, axis = 1)
+
+        return classes
+
+
+
+    def accuracy(self, y, y_pred):
+        '''Computes accuracy based on percent correct: Proportion of predicted class labels `y_pred`
+        that match the true values `y`.
+
+        Parameters:
+        -----------
+        y: ndarray. shape=(num_data_sams,)
+            Ground-truth, known class labels for each data sample
+        y_pred: ndarray. shape=(num_data_sams,)
+            Predicted class labels by the model for each data sample
+
+        Returns:
+        -----------
+        float. Between 0 and 1. Proportion correct classification.
+
+        '''
+        
+        pc = np.sum(np.where(y == y_pred, 1, 0))/y.shape[0] * 100
+
+        return pc
+
+    def confusion_matrix(self, y, y_pred):
+        '''Create a confusion matrix based on the ground truth class labels (`y`) and those predicted
+        by the classifier (`y_pred`).
+
+        Parameters:
+        -----------
+        y: ndarray. shape=(num_data_samps,)
+            Ground-truth, known class labels for each data sample
+        y_pred: ndarray. shape=(num_data_samps,)
+            Predicted class labels by the model for each data sample
+
+        Returns:
+        -----------
+        ndarray. shape=(num_classes, num_classes).
+            Confusion matrix
+        '''
+        
+        matrix = np.zeros((self.num_classes, self.num_classes))
+
+        for i in range(self.num_classes):
+            for j in range(self.num_classes):
+                act_match = np.where(y == i, 1, 0)
+                pred_match = np.where(y_pred == j, 1, 0)
+                matches = np.logical_and(act_match, pred_match)
+                matrix[i, j] = np.sum(np.where(matches == True, 1, 0))
+
+        return matrix
+    
+    def kfold(self, data, labels, k):
+        '''Perform k-fold cross validation on the data and labels. Returns an array of accuracies
+        
+        Parameters:
+        -----------
+        data: ndarray. shape=(num_data_samps, num_features).
+        labels: ndarray. shape=(num_data_samps,).
+        k: int. Number of folds to use in cross validation
+        
+        Returns:
+        -----------
+        accuracies: ndarray. shape=(k,). Array of accuracies for each fold
+        '''
+        inds = np.arange(labels.size)
+
+        # shuffle data
+        features = data.copy()
+        y = labels.copy()
+        inds = np.arange(y.size)
+        np.random.shuffle(inds)
+        features = features[inds]
+        y = y[inds]
+
+        accuracies = np.zeros(k)
+
+        # start folds
+        start = 0
+        fold = y.size//k
+
+        for i in range(k):
+            end = start + fold
+
+            # test fold
+            x_test = features[start:end, :]
+            y_test = y[start:end]
+
+    
+            # before kth fold
+            x_before = features[0:start, :]
+            y_before = y[0:start]
+
+            # after kth fold
+            x_after = features[end:, :]
+            y_after = y[end:]
+
+            # combine into training
+            x_train = np.vstack((x_before, x_after))
+            y_train = np.hstack((y_before, y_after))
+
+            # print("train", x_train.shape)
+
+            # train and eval
+            self.train(x_train, y_train)
+            y_pred = self.predict(x_test)
+            acc = self.accuracy(y_test, y_pred)
+            accuracies[i] = acc
+
+            start += fold
+        
+        return accuracies