diff --git a/labs/2019/lab3_lr_svm_eval.ipynb b/labs/2019/lab3_lr_svm_eval.ipynb
new file mode 100644
index 0000000..90decce
--- /dev/null
+++ b/labs/2019/lab3_lr_svm_eval.ipynb
@@ -0,0 +1,600 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lab 3: Logistic Regression, Support Vector Machines, and Evaluation\n",
+    "\n",
+    "\n",
+    "In this lab we'll get some hands on experience with two more classifiers we've seen in class\n",
+    "- Logitic Regression\n",
+    "- Support Vector Machines\n",
+    "\n",
+    "We will also explore evaluation metrics that we covered in class and understand how to calculate them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Goals for this lab\n",
+    "\n",
+    "- Understand the practical implications for changing the parameters used in Logistic Regression and Support Vector Machines\n",
+    "  \n",
+    "- Learn more about the evaluation metrics covered in class and learn how to calculate them (at different thresholds)\n",
+    "  - accuracy\n",
+    "  - precision\n",
+    "  - recall\n",
+    "  - AUC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sklearn.tree as tree\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.metrics import accuracy_score as accuracy\n",
+    "import graphviz # If you don't have this, install via pip/conda\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# exercise: what additional modules should you import?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data\n",
+    "We'll continue to use the same data as in the previous lab.\n",
+    "\n",
+    "It is a subset of the data set from https://www.kaggle.com/new-york-state/nys-patient-characteristics-survey-pcs-2015\n",
+    "\n",
+    "The data has been downloaded, modified, and is in the github repo for the lab\n",
+    "\n",
+    "You should also try this with other data sets you have been provided for the homeworks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Change this to wherever you're storing your data\n",
+    "datafile = '../data/nysmedicaldata.csv'\n",
+    "df = pd.read_csv(dfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Some Quick Data Exploration\n",
+    "Before running any sort of model on your dataset, it's always a good idea to do some quick data exploration to get a sense of how your data looks like. Try to answer the following questions with some sort of plot/histogram/etc:\n",
+    "\n",
+    "1) What do the distributions of each feature look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Ex\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using scikitlearn for classification\n",
+    "\n",
+    "sklearn is a very useful python packager for building machiune learning models. To build a model in sklearn, you need to have a matrix (or dataframe) with X and y columns. X is your set of features/predictors. y is a single column that is your label. We'll take the foll;owing steps:\n",
+    "\n",
+    "1. Select/create column as label/outcome (y)\n",
+    "2. Select/create columns as features (X)\n",
+    "3. Create Training Set\n",
+    "4. Create Validation Set\n",
+    "5. Build model on Training Set\n",
+    "6. Predict risk scores for the Validation Set\n",
+    "7. Calculate performance metric(s)\n",
+    "\n",
+    "## Some useful things to know in sklearn\n",
+    "\n",
+    "fit = train an algorithm\n",
+    "\n",
+    "predict_proba = predict a \"risk\" score for all possible classes for a given record (classification only)\n",
+    "\n",
+    "\n",
+    "## Important- never use .predict\n",
+    "There is also a function called \"predict\" which first runs predict_probs and then predicts a 1 if the score > 0.5 and 0 otherwise. *Never* use that function since 0.5 is a completely arbitrary threshold to call a prediction 1 vs 0.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Create label/outcome\n",
+    "One thing we can do with this dataset is to try to use the various feature columns to classify whether a person has High Blood Pressure. Let's create a column that is 1 if a person has High Blood Pressure and 0 otherwise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question: what percentage of people have High Blood Pressure?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. create or select existing predictors/features\n",
+    "\n",
+    "For now, let's take a handful of existing columns to use.\n",
+    "\n",
+    "sklearn needs features to be numeric and not categorical so we'll have to turn our selected features to be binary (also known as dummy variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train/Test Splits\n",
+    "\n",
+    "Create a train/test set split using sklearn's [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function. We'll use these train/test splits for evaluating all our classification models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Logistic Regression\n",
+    "See the sklearn documentation on Logistic Regression to see its parameters. The one's we'll mostly be interested in are:\n",
+    "- penalty\n",
+    "- C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that when training a model, **you should only use the training data!** The test set is reserved exclusively for evaluating your model. Now let's use the classifier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistic Regression Tasks:\n",
+    "\n",
+    "The goal here is to explore different penalty parameters and different C values. You can also try modofyinfg other parameters to see their impact. How does accuracy change, using different thresholds, as you vary penalty and C values? You can write a nested for loop that loops over all the parameters and values and store the results in a data frame (similar to last lab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Ref: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
+    "\n",
+    "You'll notice that LogisticRegression takes a ton of parameters. We'll play around with the \"penalty\" and \"C\" parameters.\n",
+    "If we set the penalty parameter to ['l2'](http://mathworld.wolfram.com/L2-Norm.html), sklearn's LogisticRegression model solves the following minimization problem:\n",
+    "\n",
+    "$$ \\min_{\\beta} ||\\beta||_2 + C \\sum_{i} \\log ( -y_i (X_i^T \\beta) +1)$$\n",
+    "\n",
+    "Similarly, if we set the penalty parameter to ['l1'](http://mathworld.wolfram.com/L2-Norm.html), LogisticRegression will solve the following minimization problem:\n",
+    "\n",
+    "$$\\min_{\\beta} ||\\beta||_1 + C \\sum_{i} \\log ( -y_i (X_i^T \\beta) +1)$$\n",
+    "\n",
+    "where $$||\\beta||_2 = \\sqrt { \\sum_{i} \\beta_i^2 }$$ and $$||\\beta||_1 =  \\sum_{i} | \\beta_i | $$ \n",
+    "\n",
+    "Try running logistic regression with both L1 and L2 penalties and a mix of C values. Something like $10^{-2}, 10^{-1}, 1, 10, 10^2)$ is reasonable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding what's going on inside Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To really see the difference between L1 and L2 regularization, we need to take a closer look at the models they produced. Plot a histogram of the weight values of LogisticRegression models for each C value. You can access these weight coefficients via the coef\\_ attribute in LogisticRegression. Do you notice anything interesting happening as the C value varies?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Support Vector Machines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ref: https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC\n",
+    "The SVM Classifier also takes quite a few parameters. For now we will use Linear SVMs. The model is called LinearSVC in sklearn.\n",
+    "\n",
+    "We will be playing with following parameters:\n",
+    "* C: same as above\n",
+    "\n",
+    "SVM tries to find the hyperplane that maximizes the \"margin\" between the two classes of points. The \"C\" parameter in [SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) has the same role as the \"C\" parameter in LogisticRegression: it tells you how much to penalize the \"size\" of the weight vector. Note that SVC only allows for L2 regularization.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's fit an SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now predict scores on the test set and plot the distribution of scores\n",
+    "You might notice that the function you've been using to predict so far does not work. Is another function you need to use? Which one? Why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### now we can select a threshold and calculate accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's now vary values of C and see the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We covered several evaluation metrics in class:\n",
+    "    - accuracy\n",
+    "    - precision\n",
+    "    - recall\n",
+    "    - area under curve\n",
+    "    - ROC curves\n",
+    "    \n",
+    "Although sklearn has built-in functions to calculate these metrics,\n",
+    "in this lab we want to give you an understanding of these metrics \n",
+    "by writing functions to calculate them yourself.\n",
+    "\n",
+    "Remember that accuracy, precision, and recall are calculated at a specific threshold for turning scores into 0 and 1.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Threshold\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "threshold = "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We will first create a confusion matrix based on this threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_positives =\n",
+    "false_positive =\n",
+    "true_negatives =\n",
+    "false_negatives = "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's now write functions that can calculate each metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def calculate_accuracy_at_threshold(predicted_scores, true_labels, threshold):\n",
+    "\n",
+    "\n",
+    "def calculate_precision_at_threshold(predicted_scores, true_labels, threshold):\n",
+    "\n",
+    "\n",
+    "def calculate_recall_at_threshold(predicted_scores, true_labels, threshold):\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's calculate all of these for a logistic regression model you built above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's write a function that generates the precision, recall, k (% of population) graph that we covered in class\n",
+    "<img src=\"../imgs/prk.png\">\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_precision_recall_k(predicted_scores, true_labels):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### let's plot it for the same logistic regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now we build the same graph for an svm model and compare the two. Which one is better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/labs/imgs/prk.png b/labs/imgs/prk.png
new file mode 100644
index 0000000..e346fbe
Binary files /dev/null and b/labs/imgs/prk.png differ