├── .gitignore ├── README.md ├── classification_exercise.ipynb ├── classification_lecture.ipynb ├── data ├── cheese.csv ├── congressional_voting.csv ├── titanic.csv └── travel-times.csv ├── extra_credit_roc.ipynb ├── images ├── classification.png ├── dtree.gif ├── knn.png ├── linear_regression.png ├── linear_regression_line.png └── logistic_regression.png ├── regression_exercise.ipynb ├── regression_exercise_soln.ipynb └── regression_lecture.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *,cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ml-workshop 2 | 3 | Before starting, you should install [anaconda](https://store.continuum.io/cshop/anaconda/). 4 | 5 | Here's the activities for the workshop: 6 | 7 | 1. [regression_lecture.ipynb](regression_lecture.ipynb) 8 | 2. [regression_exercise.ipynb](regression_exercise.ipynb) 9 | 3. [classification_lecture.ipynb](classification_lecture.ipynb) 10 | 4. [classification_exercise.ipynb](classification_exercise.ipynb) 11 | -------------------------------------------------------------------------------- /classification_exercise.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 1. Load in the data using `pandas`." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": false 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "### 2. Turn the values into numerical values as such:\n", 27 | "\n", 28 | "* Change the `n`'s to 0.\n", 29 | "* Change the `y`'s to 1.\n", 30 | "* Change the `?`'s to 0.5." 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": { 37 | "collapsed": false 38 | }, 39 | "outputs": [], 40 | "source": [] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "### 3. Make your `X` matrix and `y` vector:\n", 47 | "\n", 48 | "* Make a `y` vector to be 1 if the senator is republican and 0 if democrat. It should be a numpy array of length 435.\n", 49 | "* Make a `X` be all the remaining values. It should be a numpy array of dimensions 435 by 16." 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": { 56 | "collapsed": false 57 | }, 58 | "outputs": [], 59 | "source": [] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "### 4. Run the commands `y.shape` and `X.shape` to verify that your vectors are the correct sizes." 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "metadata": { 72 | "collapsed": false 73 | }, 74 | "outputs": [], 75 | "source": [] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "### 5. Use `sklearns`'s `Kfold` method.\n", 82 | "\n", 83 | "* You need to pass it the number of datapoints." 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 2, 89 | "metadata": { 90 | "collapsed": true 91 | }, 92 | "outputs": [], 93 | "source": [ 94 | "from sklearn.cross_validation import KFold\n", 95 | "\n" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "#### We'll use the same `run_model` function as we did with the previous example. Code is copied here for your convenience." 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 3, 108 | "metadata": { 109 | "collapsed": true 110 | }, 111 | "outputs": [], 112 | "source": [ 113 | "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", 114 | "\n", 115 | "def run_model(X, y, kfolds, Model):\n", 116 | " accuracies = []\n", 117 | " precisions = []\n", 118 | " recalls = []\n", 119 | " for train_index, test_index in kfolds:\n", 120 | " X_train, X_test = X[train_index], X[test_index]\n", 121 | " y_train, y_test = y[train_index], y[test_index]\n", 122 | " model = Model()\n", 123 | " model.fit(X_train, y_train)\n", 124 | " y_predict = model.predict(X_test)\n", 125 | " accuracies.append(accuracy_score(y_predict, y_test))\n", 126 | " precisions.append(precision_score(y_predict, y_test))\n", 127 | " recalls.append(recall_score(y_predict, y_test))\n", 128 | "\n", 129 | " print \"accuracy:\", np.mean(accuracies)\n", 130 | " print \"precision:\", np.mean(precisions)\n", 131 | " print \"recall:\", np.mean(recalls)" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "### 6. Build a logistic regression model on each of the folds and give the average values for accuracy, precision and recall." 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 4, 144 | "metadata": { 145 | "collapsed": false 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "from sklearn.linear_model import LogisticRegression\n" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "### 7. Using the same kfolds, build a Decision Tree and Random Forest classifier to compare which is the best." 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 5, 162 | "metadata": { 163 | "collapsed": false 164 | }, 165 | "outputs": [], 166 | "source": [ 167 | "from sklearn.tree import DecisionTreeClassifier\n", 168 | "from sklearn.ensemble import RandomForestClassifier\n" 169 | ] 170 | } 171 | ], 172 | "metadata": { 173 | "kernelspec": { 174 | "display_name": "Python 2", 175 | "language": "python", 176 | "name": "python2" 177 | }, 178 | "language_info": { 179 | "codemirror_mode": { 180 | "name": "ipython", 181 | "version": 2 182 | }, 183 | "file_extension": ".py", 184 | "mimetype": "text/x-python", 185 | "name": "python", 186 | "nbconvert_exporter": "python", 187 | "pygments_lexer": "ipython2", 188 | "version": "2.7.9" 189 | } 190 | }, 191 | "nbformat": 4, 192 | "nbformat_minor": 0 193 | } 194 | -------------------------------------------------------------------------------- /classification_lecture.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Classification\n", 12 | "\n", 13 | "A classification problem is when we're trying to predict a discrete (categorical) outcome.\n", 14 | "\n", 15 | "Here are some example questions:\n", 16 | "\n", 17 | "* Does a patient have cancer?\n", 18 | "* Will a team win the next game?\n", 19 | "* Will the customer buy my product?\n", 20 | "* Will I get the loan?\n", 21 | "\n", 22 | "In binary classification, we have two labels: 1 or 0." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": { 28 | "slideshow": { 29 | "slide_type": "fragment" 30 | } 31 | }, 32 | "source": [ 33 | "## Classification Algorithms\n", 34 | "\n", 35 | "1. Logistic Regression\n", 36 | "2. Decision Trees\n", 37 | "3. Random Forests" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": { 43 | "slideshow": { 44 | "slide_type": "slide" 45 | } 46 | }, 47 | "source": [ 48 | "### Logistic Regression\n", 49 | "\n", 50 | "Find a line which separates the data.\n", 51 | "\n", 52 | "![Classification datapoints](images/classification.png)\n", 53 | "\n", 54 | "There are two classes of data points:\n", 55 | "\n", 56 | "* red circles\n", 57 | "* blue pluses" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": { 63 | "slideshow": { 64 | "slide_type": "slide" 65 | } 66 | }, 67 | "source": [ 68 | "### Logistic Regression\n", 69 | "\n", 70 | "![Logistic Regression decision boundary](images/logistic_regression.png)" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": { 76 | "slideshow": { 77 | "slide_type": "slide" 78 | } 79 | }, 80 | "source": [ 81 | "### Decision Trees\n", 82 | "\n", 83 | "![Decision Tree Example](images/dtree.gif)\n", 84 | "\n", 85 | "(If you flip it upside down it will look more like a tree.)" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": { 91 | "slideshow": { 92 | "slide_type": "slide" 93 | } 94 | }, 95 | "source": [ 96 | "## Decision Trees: Choosing which feature to split on\n", 97 | "\n", 98 | "A good split:\n", 99 | "* All those with a criminal record shouldn't be given loans and all those without a record should be given loans\n", 100 | "\n", 101 | "A bad split:\n", 102 | "* 50% of women should be given a loan and 50% of men should be given a loan" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": { 108 | "slideshow": { 109 | "slide_type": "slide" 110 | } 111 | }, 112 | "source": [ 113 | "## Downside of Decision Trees: Overfitting\n", 114 | "\n", 115 | "There could just be a single datapoint that has income >$70K and has a criminal record.\n", 116 | "\n", 117 | "We can't extrapolate that all datapoints in this bucket would have the same result." 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": { 123 | "slideshow": { 124 | "slide_type": "fragment" 125 | } 126 | }, 127 | "source": [ 128 | "## Solution: Random Forests\n", 129 | "\n", 130 | "A collection of trees, often 10 trees.\n", 131 | "\n", 132 | "* **Bootstrap Aggregation (Bagging):** Each tree gets a random sample *with replacement* of the dataset to build the tree with.\n", 133 | "\n", 134 | "* **Random subset of features:** Only consider a subset of the features when finding the best one to split on." 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": { 140 | "slideshow": { 141 | "slide_type": "slide" 142 | } 143 | }, 144 | "source": [ 145 | "# Metrics\n", 146 | "\n", 147 | "We have a couple different metrics we use to evaluate how good the model is:\n", 148 | "\n", 149 | "**Accuracy:** This is the percent of predictions that were correct." 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": { 155 | "slideshow": { 156 | "slide_type": "fragment" 157 | } 158 | }, 159 | "source": [ 160 | "**Precision:** This is the fraction of datapoints that you predicted positively that are correct.\n", 161 | "\n", 162 | "```\n", 163 | " number predicted positively that are truly positive\n", 164 | "------------------------------------------------------\n", 165 | " number predicted positively (including misses)\n", 166 | "```\n", 167 | "\n", 168 | "**Recall:** This is the fraction of datapoints that are truely positive that you predicted correctly.\n", 169 | "\n", 170 | "```\n", 171 | " number predicted positively that are truly positive\n", 172 | "------------------------------------------------------\n", 173 | " number of positives\n", 174 | "```" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "metadata": { 180 | "slideshow": { 181 | "slide_type": "slide" 182 | } 183 | }, 184 | "source": [ 185 | "# Example: Titanic\n", 186 | "\n", 187 | "Goal: predict if someone survives or not\n", 188 | "\n", 189 | "```\n", 190 | "VARIABLE DESCRIPTIONS:\n", 191 | "survival Survival\n", 192 | " (0 = No; 1 = Yes)\n", 193 | "pclass Passenger Class\n", 194 | " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", 195 | "name Name\n", 196 | "sex Sex\n", 197 | "age Age\n", 198 | "sibsp Number of Siblings/Spouses Aboard\n", 199 | "parch Number of Parents/Children Aboard\n", 200 | "ticket Ticket Number\n", 201 | "fare Passenger Fare\n", 202 | "cabin Cabin\n", 203 | "embarked Port of Embarkation\n", 204 | " (C = Cherbourg; Q = Queenstown; S = Southampton)\n", 205 | "```" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 1, 211 | "metadata": { 212 | "collapsed": false, 213 | "scrolled": true, 214 | "slideshow": { 215 | "slide_type": "slide" 216 | } 217 | }, 218 | "outputs": [ 219 | { 220 | "data": { 221 | "text/html": [ 222 | "
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survivedpclassnamesexagesibspparchticketfarecabinembarked
0 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S
1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C
2 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S
3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S
4 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 NaN S
\n", 313 | "
" 314 | ], 315 | "text/plain": [ 316 | " survived pclass name \\\n", 317 | "0 0 3 Braund, Mr. Owen Harris \n", 318 | "1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", 319 | "2 1 3 Heikkinen, Miss. Laina \n", 320 | "3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", 321 | "4 0 3 Allen, Mr. William Henry \n", 322 | "\n", 323 | " sex age sibsp parch ticket fare cabin embarked \n", 324 | "0 male 22 1 0 A/5 21171 7.2500 NaN S \n", 325 | "1 female 38 1 0 PC 17599 71.2833 C85 C \n", 326 | "2 female 26 0 0 STON/O2. 3101282 7.9250 NaN S \n", 327 | "3 female 35 1 0 113803 53.1000 C123 S \n", 328 | "4 male 35 0 0 373450 8.0500 NaN S " 329 | ] 330 | }, 331 | "execution_count": 1, 332 | "metadata": {}, 333 | "output_type": "execute_result" 334 | } 335 | ], 336 | "source": [ 337 | "import pandas as pd\n", 338 | "import numpy as np\n", 339 | "\n", 340 | "df = pd.read_csv('data/titanic.csv')\n", 341 | "df.head()" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 2, 347 | "metadata": { 348 | "collapsed": false, 349 | "slideshow": { 350 | "slide_type": "slide" 351 | } 352 | }, 353 | "outputs": [ 354 | { 355 | "data": { 356 | "text/html": [ 357 | "
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survivedpclassagesibspparchfare
count 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200
\n", 445 | "
" 446 | ], 447 | "text/plain": [ 448 | " survived pclass age sibsp parch fare\n", 449 | "count 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000\n", 450 | "mean 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208\n", 451 | "std 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429\n", 452 | "min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000\n", 453 | "25% 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400\n", 454 | "50% 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200\n", 455 | "75% 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000\n", 456 | "max 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200" 457 | ] 458 | }, 459 | "execution_count": 2, 460 | "metadata": {}, 461 | "output_type": "execute_result" 462 | } 463 | ], 464 | "source": [ 465 | "# stats on the data\n", 466 | "df.describe()" 467 | ] 468 | }, 469 | { 470 | "cell_type": "markdown", 471 | "metadata": { 472 | "slideshow": { 473 | "slide_type": "fragment" 474 | } 475 | }, 476 | "source": [ 477 | "* There are 891 datapoints\n", 478 | "* 38% of the people survived\n", 479 | "* Only 714 of the 891 datapoints have the age feature filled in" 480 | ] 481 | }, 482 | { 483 | "cell_type": "code", 484 | "execution_count": 3, 485 | "metadata": { 486 | "collapsed": false, 487 | "slideshow": { 488 | "slide_type": "slide" 489 | } 490 | }, 491 | "outputs": [ 492 | { 493 | "data": { 494 | "text/html": [ 495 | "
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survivedpclassnamesexagesibspparchticketfarecabinembarkedCQSfemaleage_filled
0 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S 0 0 1 False 22
1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C 1 0 0 True 38
2 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S 0 0 1 True 26
3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S 0 0 1 True 35
4 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 NaN S 0 0 1 False 35
\n", 616 | "
" 617 | ], 618 | "text/plain": [ 619 | " survived pclass name \\\n", 620 | "0 0 3 Braund, Mr. Owen Harris \n", 621 | "1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", 622 | "2 1 3 Heikkinen, Miss. Laina \n", 623 | "3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", 624 | "4 0 3 Allen, Mr. William Henry \n", 625 | "\n", 626 | " sex age sibsp parch ticket fare cabin embarked C Q \\\n", 627 | "0 male 22 1 0 A/5 21171 7.2500 NaN S 0 0 \n", 628 | "1 female 38 1 0 PC 17599 71.2833 C85 C 1 0 \n", 629 | "2 female 26 0 0 STON/O2. 3101282 7.9250 NaN S 0 0 \n", 630 | "3 female 35 1 0 113803 53.1000 C123 S 0 0 \n", 631 | "4 male 35 0 0 373450 8.0500 NaN S 0 0 \n", 632 | "\n", 633 | " S female age_filled \n", 634 | "0 1 False 22 \n", 635 | "1 0 True 38 \n", 636 | "2 1 True 26 \n", 637 | "3 1 True 35 \n", 638 | "4 1 False 35 " 639 | ] 640 | }, 641 | "execution_count": 3, 642 | "metadata": {}, 643 | "output_type": "execute_result" 644 | } 645 | ], 646 | "source": [ 647 | "df = pd.concat([df, pd.get_dummies(df['embarked'])], axis=1)\n", 648 | "df['female'] = df['sex'] == 'female'\n", 649 | "df['age_filled'] = df['age'].fillna(df['age'].mean())\n", 650 | "df.head()" 651 | ] 652 | }, 653 | { 654 | "cell_type": "code", 655 | "execution_count": 4, 656 | "metadata": { 657 | "collapsed": false, 658 | "slideshow": { 659 | "slide_type": "slide" 660 | } 661 | }, 662 | "outputs": [ 663 | { 664 | "name": "stdout", 665 | "output_type": "stream", 666 | "text": [ 667 | "X dimensions: (891, 9)\n", 668 | "y dimensions: (891,)\n" 669 | ] 670 | } 671 | ], 672 | "source": [ 673 | "features = ['pclass', 'age_filled', 'sibsp', 'parch', 'fare', 'C', 'Q', 'S', 'female']\n", 674 | "X = df[features].values.astype(float)\n", 675 | "y = df['survived'].values\n", 676 | "\n", 677 | "print \"X dimensions:\", X.shape\n", 678 | "print \"y dimensions:\", y.shape" 679 | ] 680 | }, 681 | { 682 | "cell_type": "code", 683 | "execution_count": 5, 684 | "metadata": { 685 | "collapsed": false, 686 | "slideshow": { 687 | "slide_type": "slide" 688 | } 689 | }, 690 | "outputs": [ 691 | { 692 | "name": "stdout", 693 | "output_type": "stream", 694 | "text": [ 695 | "Logistic Regression:\n", 696 | "accuracy: 0.789001122334\n", 697 | "precision: 0.675874970883\n", 698 | "recall: 0.747860965872\n" 699 | ] 700 | } 701 | ], 702 | "source": [ 703 | "from sklearn.cross_validation import KFold\n", 704 | "from sklearn.linear_model import LogisticRegression\n", 705 | "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", 706 | "\n", 707 | "kfolds = KFold(len(X))\n", 708 | "\n", 709 | "def run_model(X, y, kfolds, Model):\n", 710 | " accuracies = []\n", 711 | " precisions = []\n", 712 | " recalls = []\n", 713 | " for train_index, test_index in kfolds:\n", 714 | " X_train, X_test = X[train_index], X[test_index]\n", 715 | " y_train, y_test = y[train_index], y[test_index]\n", 716 | " model = Model()\n", 717 | " model.fit(X_train, y_train)\n", 718 | " y_predict = model.predict(X_test)\n", 719 | " accuracies.append(accuracy_score(y_predict, y_test))\n", 720 | " precisions.append(precision_score(y_predict, y_test))\n", 721 | " recalls.append(recall_score(y_predict, y_test))\n", 722 | "\n", 723 | " print \"accuracy:\", np.mean(accuracies)\n", 724 | " print \"precision:\", np.mean(precisions)\n", 725 | " print \"recall:\", np.mean(recalls)\n", 726 | "\n", 727 | "print \"Logistic Regression:\"\n", 728 | "run_model(X, y, kfolds, LogisticRegression)\n" 729 | ] 730 | }, 731 | { 732 | "cell_type": "code", 733 | "execution_count": 6, 734 | "metadata": { 735 | "collapsed": false, 736 | "slideshow": { 737 | "slide_type": "slide" 738 | } 739 | }, 740 | "outputs": [ 741 | { 742 | "name": "stdout", 743 | "output_type": "stream", 744 | "text": [ 745 | "Decision Tree:\n", 746 | "accuracy: 0.757575757576\n", 747 | "precision: 0.670857718961\n", 748 | "recall: 0.686929824561\n" 749 | ] 750 | } 751 | ], 752 | "source": [ 753 | "from sklearn.tree import DecisionTreeClassifier\n", 754 | "\n", 755 | "print \"Decision Tree:\"\n", 756 | "run_model(X, y, kfolds, DecisionTreeClassifier)" 757 | ] 758 | }, 759 | { 760 | "cell_type": "code", 761 | "execution_count": 7, 762 | "metadata": { 763 | "collapsed": false, 764 | "slideshow": { 765 | "slide_type": "fragment" 766 | } 767 | }, 768 | "outputs": [ 769 | { 770 | "name": "stdout", 771 | "output_type": "stream", 772 | "text": [ 773 | "Random Forest:\n", 774 | "accuracy: 0.79797979798\n", 775 | "precision: 0.683279830538\n", 776 | "recall: 0.759372435843\n" 777 | ] 778 | } 779 | ], 780 | "source": [ 781 | "from sklearn.ensemble import RandomForestClassifier\n", 782 | "\n", 783 | "print \"Random Forest:\"\n", 784 | "run_model(X, y, kfolds, RandomForestClassifier)" 785 | ] 786 | }, 787 | { 788 | "cell_type": "markdown", 789 | "metadata": { 790 | "slideshow": { 791 | "slide_type": "slide" 792 | } 793 | }, 794 | "source": [ 795 | "Let's see if we can add some additional features that will help us. This is called *feature engineering*.\n", 796 | "\n", 797 | "1. A missing age might mean something!\n", 798 | "2. The length of the name might have some status.\n", 799 | "3. Use the cabin column. Did they have an assigned cabin?" 800 | ] 801 | }, 802 | { 803 | "cell_type": "code", 804 | "execution_count": 8, 805 | "metadata": { 806 | "collapsed": false, 807 | "slideshow": { 808 | "slide_type": "fragment" 809 | } 810 | }, 811 | "outputs": [], 812 | "source": [ 813 | "df['missing_age'] = pd.isnull(df['age'])\n", 814 | "df['name_length'] = df['name'].apply(lambda x: len(x))\n", 815 | "df['no_cabin'] = pd.isnull(df['cabin'])" 816 | ] 817 | }, 818 | { 819 | "cell_type": "code", 820 | "execution_count": 9, 821 | "metadata": { 822 | "collapsed": false, 823 | "slideshow": { 824 | "slide_type": "fragment" 825 | } 826 | }, 827 | "outputs": [ 828 | { 829 | "name": "stdout", 830 | "output_type": "stream", 831 | "text": [ 832 | "Random Forest:\n", 833 | "accuracy: 0.794612794613\n", 834 | "precision: 0.660883269276\n", 835 | "recall: 0.769083820663\n" 836 | ] 837 | } 838 | ], 839 | "source": [ 840 | "new_features = features + ['missing_age', 'name_length', 'no_cabin']\n", 841 | "X = df[new_features].values.astype(float)\n", 842 | "y = df['survived'].values\n", 843 | "\n", 844 | "print \"Random Forest:\"\n", 845 | "run_model(X, y, kfolds, RandomForestClassifier)" 846 | ] 847 | }, 848 | { 849 | "cell_type": "code", 850 | "execution_count": 10, 851 | "metadata": { 852 | "collapsed": false, 853 | "slideshow": { 854 | "slide_type": "slide" 855 | } 856 | }, 857 | "outputs": [ 858 | { 859 | "name": "stdout", 860 | "output_type": "stream", 861 | "text": [ 862 | "feature importances:\n" 863 | ] 864 | }, 865 | { 866 | "data": { 867 | "text/plain": [ 868 | "[(0.24996092924865998, 'female'),\n", 869 | " (0.18060870673998769, 'fare'),\n", 870 | " (0.17764809383355634, 'age_filled'),\n", 871 | " (0.16876470705763175, 'name_length'),\n", 872 | " (0.075846096630100765, 'pclass'),\n", 873 | " (0.036383190959322818, 'sibsp'),\n", 874 | " (0.031386514124276281, 'no_cabin'),\n", 875 | " (0.029385544726632774, 'parch'),\n", 876 | " (0.015162873337054216, 'missing_age'),\n", 877 | " (0.01500098039067875, 'S'),\n", 878 | " (0.013131614687145129, 'C'),\n", 879 | " (0.0067207482649535603, 'Q')]" 880 | ] 881 | }, 882 | "execution_count": 10, 883 | "metadata": {}, 884 | "output_type": "execute_result" 885 | } 886 | ], 887 | "source": [ 888 | "print \"feature importances:\"\n", 889 | "rf_model = RandomForestClassifier().fit(X, y)\n", 890 | "sorted(zip(rf_model.feature_importances_, new_features), reverse=True)" 891 | ] 892 | } 893 | ], 894 | "metadata": { 895 | "celltoolbar": "Slideshow", 896 | "kernelspec": { 897 | "display_name": "Python 2", 898 | "language": "python", 899 | "name": "python2" 900 | }, 901 | "language_info": { 902 | "codemirror_mode": { 903 | "name": "ipython", 904 | "version": 2 905 | }, 906 | "file_extension": ".py", 907 | "mimetype": "text/x-python", 908 | "name": "python", 909 | "nbconvert_exporter": "python", 910 | "pygments_lexer": "ipython2", 911 | "version": "2.7.9" 912 | } 913 | }, 914 | "nbformat": 4, 915 | "nbformat_minor": 0 916 | } 917 | -------------------------------------------------------------------------------- /data/cheese.csv: -------------------------------------------------------------------------------- 1 | Case,taste,Acetic,H2S,Lactic 2 | 1,12.3,93.97229455035794,22.988635842034803,0.86 3 | 2,20.9,173.9903782027133,154.93412088167287,1.53 4 | 3,39.0,214.00513284687926,229.98175967098499,1.57 5 | 4,47.9,317.0311392102188,1800.82468987104,1.81 6 | 5,5.6,105.95346624121831,45.01519052366997,0.99 7 | 6,25.9,297.97214232356816,2000.1950904302855,1.09 8 | 7,37.3,362.12881823371424,6161.0346186664965,1.29 9 | 8,21.9,436.15600980492945,2881.309046796117,1.78 10 | 9,18.1,134.02146852616139,46.993063231579285,1.29 11 | 10,21.0,189.04782020236823,64.97483233043849,1.58 12 | 11,34.9,311.06441098139294,464.9826067271843,1.68 13 | 12,57.2,630.1765385172663,2718.9471305583256,1.9 14 | 13,0.7,87.97036531689318,20.005355245758658,1.06 15 | 14,25.9,187.91692934639244,140.05006978607565,1.3 16 | 15,54.9,469.18633860603165,855.768589308044,1.52 17 | 16,40.9,581.1448283170228,14588.66301153218,1.74 18 | 17,15.9,119.9410053724344,49.99884974182383,1.16 19 | 18,6.4,224.07929841740724,109.94717245212352,1.49 20 | 19,18.0,189.99542634455688,480.1026811082229,1.63 21 | 20,38.9,229.98175967098499,8638.63618171464,1.99 22 | 21,14.0,95.96657943780268,141.03385952151402,1.15 23 | 22,15.2,199.93653676147963,184.9341840706834,1.33 24 | 23,32.0,233.92487106947016,10321.661250847777,1.44 25 | 24,56.7,348.9749000539856,26876.296335333554,2.01 26 | 25,16.8,214.00513284687926,39.017099548496596,1.31 27 | 26,11.6,421.1546054009281,25.003104571046247,1.46 28 | 27,26.5,637.7842117268708,1055.7429330294667,1.72 29 | 28,0.7,206.02551080883075,49.99884974182383,1.25 30 | 29,13.4,330.9608200692076,800.3106781756247,1.08 31 | 30,5.5,481.063847316259,119.9410053724344,1.25 32 | -------------------------------------------------------------------------------- /data/congressional_voting.csv: 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democrat,y,y,y,n,n,?,y,y,y,y,n,n,n,n,y,? 63 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n 64 | democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,n,? 65 | democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,n,y 66 | democrat,y,y,y,n,n,n,y,y,y,n,y,?,n,n,n,y 67 | republican,y,y,n,y,y,y,y,n,n,n,n,y,y,y,n,y 68 | republican,n,y,n,y,y,y,y,n,n,n,y,y,y,y,n,y 69 | republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n 70 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,y,n,y,y 71 | democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y 72 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,y,n,y,? 73 | republican,y,y,y,y,n,n,y,y,y,y,y,n,n,y,n,y 74 | democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,y,? 75 | republican,y,n,y,y,y,n,y,n,y,y,n,n,y,y,n,y 76 | democrat,y,n,y,n,n,y,y,y,y,y,y,n,n,y,y,y 77 | democrat,n,y,y,y,y,y,n,n,n,y,y,n,y,y,n,n 78 | democrat,n,y,y,n,y,y,n,n,n,y,y,y,y,y,n,? 79 | democrat,n,y,y,y,y,y,n,y,y,y,y,y,y,y,n,y 80 | democrat,y,y,y,n,y,y,n,n,n,y,y,n,y,y,n,y 81 | republican,n,n,n,y,y,n,n,n,n,y,n,y,y,y,n,n 82 | democrat,y,n,y,n,n,y,y,y,y,y,n,y,n,y,n,? 83 | democrat,y,n,y,n,n,n,y,y,?,y,y,y,n,y,n,y 84 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y 85 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n 86 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,n 87 | democrat,n,n,y,n,y,y,n,n,n,y,y,y,y,y,n,y 88 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n 89 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n 90 | democrat,n,y,y,n,y,y,y,n,y,y,y,n,y,y,n,y 91 | republican,n,n,n,y,y,y,n,n,n,y,n,?,y,y,n,? 92 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y 93 | democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y 94 | democrat,y,y,y,n,n,n,y,y,n,y,y,n,n,?,y,y 95 | democrat,y,n,y,n,n,n,y,n,y,y,y,n,n,n,y,y 96 | democrat,y,n,y,n,y,y,n,n,n,n,n,n,n,n,n,y 97 | democrat,y,n,y,n,y,y,n,?,?,n,y,?,?,?,y,y 98 | democrat,n,n,?,n,y,y,n,n,n,n,y,y,y,y,n,y 99 | democrat,y,n,n,n,y,y,y,n,n,y,y,n,n,y,n,y 100 | democrat,y,y,y,n,n,y,y,y,y,y,n,n,n,n,n,y 101 | republican,n,n,n,y,y,y,n,n,n,y,?,y,y,y,n,n 102 | democrat,y,n,n,n,y,y,n,n,n,n,y,y,n,y,n,y 103 | democrat,y,n,y,n,y,y,y,n,n,n,y,n,n,y,n,y 104 | democrat,y,n,y,n,y,y,y,n,?,n,y,n,y,y,y,? 105 | democrat,y,n,n,n,y,y,?,n,?,n,n,n,n,y,?,n 106 | democrat,?,?,?,?,n,y,y,y,y,y,?,n,y,y,n,? 107 | democrat,y,y,y,n,n,n,n,y,y,n,y,n,n,n,y,y 108 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y 109 | republican,n,?,?,?,?,?,?,?,?,?,?,?,?,y,?,? 110 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? 111 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? 112 | democrat,n,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y 113 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y 114 | democrat,n,?,y,n,n,y,y,y,n,y,n,n,n,n,y,? 115 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,n 116 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? 117 | democrat,n,?,y,n,?,?,y,y,y,y,?,?,n,n,y,y 118 | democrat,y,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y 119 | republican,y,y,y,y,y,n,y,n,n,n,n,y,y,y,n,y 120 | democrat,n,y,y,n,n,n,n,y,y,y,y,n,n,n,y,y 121 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n 122 | republican,n,?,?,y,y,y,n,n,n,y,n,y,y,y,?,y 123 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y 124 | republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y 125 | republican,y,?,n,y,y,y,n,y,n,n,n,y,y,y,n,y 126 | democrat,n,?,y,n,n,n,y,y,y,n,n,n,n,n,y,y 127 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y 128 | republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,n 129 | democrat,n,?,y,n,n,n,y,y,y,y,y,n,n,y,y,y 130 | democrat,n,?,y,n,n,y,n,y,n,y,y,n,n,n,y,y 131 | democrat,?,?,y,n,n,n,y,y,?,n,?,?,?,?,?,? 132 | democrat,y,?,y,n,?,?,y,y,y,n,n,n,n,n,y,? 133 | democrat,n,n,y,n,n,y,n,y,y,y,n,n,n,y,n,y 134 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,? 135 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y 136 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,? 137 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n 138 | republican,n,y,n,y,y,y,n,n,n,y,y,y,y,n,n,y 139 | democrat,n,?,y,n,n,y,y,y,y,y,n,n,n,y,y,y 140 | democrat,n,n,y,n,n,y,y,y,y,y,n,n,n,y,n,y 141 | democrat,y,n,y,n,n,y,y,y,y,n,n,n,n,n,y,y 142 | republican,n,n,n,y,n,n,y,y,y,y,n,n,y,y,n,y 143 | republican,n,n,n,y,y,y,y,y,y,y,n,y,y,y,?,y 144 | republican,n,n,n,y,y,y,y,y,y,y,n,y,y,y,n,y 145 | democrat,?,y,n,n,n,n,y,y,y,y,y,n,n,y,y,y 146 | democrat,n,?,n,n,n,y,y,y,y,y,n,n,n,y,n,? 147 | democrat,n,n,y,n,n,y,y,y,y,y,n,n,n,y,?,y 148 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y 149 | democrat,n,n,n,n,n,n,y,y,y,y,n,y,y,y,y,y 150 | republican,n,y,n,y,y,y,n,n,n,y,y,y,y,y,n,y 151 | democrat,n,n,y,n,n,n,y,y,y,y,n,n,y,n,y,y 152 | republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y 153 | democrat,y,y,?,y,y,y,n,n,y,n,y,?,y,y,n,n 154 | democrat,n,y,y,n,n,y,n,y,y,y,y,n,y,n,y,y 155 | democrat,n,n,y,n,n,y,y,y,y,y,y,n,y,y,n,y 156 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n 157 | republican,y,y,n,y,y,y,n,?,n,n,y,y,y,y,n,n 158 | republican,y,y,n,y,y,y,y,n,n,n,n,y,y,y,n,n 159 | democrat,n,y,y,n,n,y,n,y,y,n,y,n,?,?,?,? 160 | republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n 161 | democrat,n,y,y,n,?,y,y,y,y,y,y,n,n,?,n,? 162 | democrat,n,y,n,n,y,y,n,n,n,n,n,y,y,y,y,y 163 | democrat,n,n,n,n,y,y,y,n,n,n,n,y,y,y,n,y 164 | democrat,n,y,y,n,y,y,y,n,n,n,y,y,y,y,n,y 165 | republican,n,y,n,y,y,y,y,n,n,n,n,y,y,y,n,y 166 | democrat,y,y,n,n,y,y,n,n,n,y,y,y,y,y,n,? 167 | democrat,n,y,y,n,n,y,y,y,y,y,y,n,y,n,y,? 168 | republican,y,n,y,y,y,y,y,y,n,y,n,y,n,y,y,y 169 | republican,y,n,y,y,y,y,y,y,n,y,y,y,n,y,y,y 170 | democrat,n,n,y,y,y,y,n,n,y,n,n,n,y,y,y,? 171 | democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,y,n,y 172 | democrat,y,n,y,n,n,n,?,y,y,?,n,n,n,n,y,? 173 | republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y 174 | democrat,n,y,y,n,n,n,y,y,y,y,n,n,?,n,y,y 175 | democrat,n,n,n,n,y,y,n,n,n,y,y,y,y,y,n,y 176 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? 177 | democrat,n,y,y,n,n,n,y,y,y,y,n,n,n,n,y,y 178 | republican,n,n,y,y,n,n,y,y,y,y,n,n,n,y,y,y 179 | democrat,n,n,y,n,n,n,y,y,y,y,y,?,n,n,y,y 180 | democrat,?,n,y,n,n,n,y,y,y,y,y,?,n,n,y,? 181 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y 182 | democrat,?,?,y,n,n,n,y,y,y,?,?,n,n,n,?,? 183 | democrat,n,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y 184 | democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,y 185 | democrat,?,?,?,?,?,?,?,?,y,?,?,?,?,?,?,? 186 | democrat,n,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y 187 | democrat,y,n,y,n,n,n,y,y,y,y,n,?,n,n,y,y 188 | democrat,n,y,y,n,n,n,y,y,y,y,y,n,n,n,y,y 189 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 190 | republican,y,?,n,y,y,y,y,y,n,n,n,y,?,y,?,? 191 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y 192 | republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,? 193 | republican,n,y,n,y,y,y,n,?,n,y,n,y,y,y,n,? 194 | democrat,n,n,n,n,n,y,y,y,y,n,y,n,n,y,y,y 195 | democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y 196 | democrat,n,n,y,n,n,y,y,?,y,y,y,n,n,n,y,y 197 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,? 198 | democrat,n,n,y,n,n,y,y,y,y,n,y,y,n,y,y,? 199 | republican,n,?,y,y,y,y,n,n,n,y,n,n,n,y,n,y 200 | democrat,n,n,y,n,n,n,y,y,y,y,y,n,?,n,y,? 201 | democrat,y,y,n,n,n,n,y,y,?,n,y,n,n,n,y,? 202 | democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,y,y,y 203 | democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y 204 | democrat,y,n,y,n,n,y,y,y,y,y,y,n,n,n,y,y 205 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y 206 | republican,n,n,y,y,y,y,y,n,n,n,n,y,y,y,n,y 207 | democrat,n,n,y,n,n,y,y,y,y,y,n,y,n,n,n,y 208 | republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y 209 | republican,y,?,n,y,y,y,y,n,n,y,n,y,y,y,n,y 210 | democrat,n,n,y,n,n,n,y,y,y,n,n,?,n,n,y,y 211 | democrat,y,y,y,n,n,n,y,y,y,y,y,n,n,n,n,y 212 | democrat,n,n,y,n,n,y,y,y,y,n,n,n,n,n,y,y 213 | republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y 214 | democrat,n,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y 215 | democrat,n,y,y,n,n,y,n,y,y,n,y,n,y,n,y,y 216 | republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y 217 | democrat,n,y,y,y,y,y,n,n,n,y,y,y,y,y,y,? 218 | democrat,y,y,y,n,y,y,n,n,?,y,n,n,n,y,y,? 219 | republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n 220 | democrat,y,?,y,n,n,n,y,y,y,n,?,n,n,n,y,? 221 | democrat,n,y,y,n,n,n,n,y,y,n,y,n,n,y,y,y 222 | democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 223 | democrat,n,y,y,n,y,y,n,n,n,n,y,n,n,n,y,? 224 | democrat,y,n,y,n,n,n,y,y,y,n,y,n,n,n,y,? 225 | republican,n,n,n,y,y,n,n,n,n,n,n,y,y,y,n,y 226 | republican,n,y,n,y,y,y,n,n,n,y,n,?,y,y,n,n 227 | republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,y 228 | democrat,n,n,y,n,n,y,y,y,y,n,y,n,n,y,y,y 229 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,?,y 230 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,?,n,y 231 | republican,n,y,y,y,y,y,y,n,y,y,n,y,y,y,n,y 232 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y 233 | republican,n,y,n,y,y,y,n,n,y,y,n,y,y,y,n,y 234 | democrat,n,y,y,n,n,n,y,y,n,n,y,n,n,n,y,? 235 | republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y 236 | democrat,n,n,y,n,n,y,y,y,y,y,n,y,n,y,y,? 237 | republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y 238 | democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y 239 | democrat,y,n,y,n,n,y,y,y,n,n,n,y,y,n,n,y 240 | democrat,y,y,y,n,n,n,y,y,?,y,n,n,n,n,y,? 241 | republican,n,n,n,y,y,y,y,n,n,y,n,n,n,y,y,y 242 | republican,n,n,n,y,n,y,y,?,y,n,n,y,y,y,n,y 243 | democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,y,y,y 244 | republican,n,n,n,n,y,y,y,n,n,n,n,?,n,y,y,y 245 | democrat,n,y,y,n,n,n,y,y,?,y,n,n,y,n,y,y 246 | democrat,y,n,y,n,n,n,n,y,y,y,n,n,n,n,y,y 247 | democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y 248 | democrat,n,n,y,n,y,n,y,y,y,n,n,n,n,y,?,y 249 | republican,n,y,n,y,y,y,?,n,n,n,n,?,y,y,n,n 250 | republican,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,? 251 | democrat,y,n,y,n,n,n,y,y,?,n,y,n,n,n,y,y 252 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n 253 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n 254 | democrat,y,y,y,n,n,y,y,y,y,n,n,n,n,n,y,y 255 | republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y 256 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y 257 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,y,y,y 258 | republican,n,n,n,y,y,n,n,n,n,n,n,y,n,y,n,n 259 | republican,n,n,n,y,y,n,n,n,n,n,n,y,n,y,?,y 260 | democrat,n,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y 261 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,n,y 262 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y 263 | democrat,y,n,y,n,n,?,y,y,y,n,?,?,n,?,?,? 264 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,?,n,y,y 265 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 266 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 267 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y 268 | republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y 269 | republican,y,n,n,n,n,n,y,y,y,y,n,n,n,y,n,y 270 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 271 | democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,n,y 272 | democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y 273 | democrat,n,y,y,n,n,y,y,y,y,n,?,n,n,n,n,y 274 | democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,? 275 | republican,n,n,n,y,y,n,y,y,n,y,n,y,y,y,?,y 276 | republican,y,n,n,y,y,n,y,n,n,y,n,n,n,y,y,y 277 | democrat,n,n,y,n,y,y,n,n,n,n,?,n,y,y,n,n 278 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,y,n 279 | republican,n,n,y,y,y,y,y,y,n,y,n,n,n,y,n,y 280 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y 281 | republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n 282 | democrat,n,n,y,n,n,n,y,y,y,y,n,n,n,y,n,y 283 | republican,y,n,y,y,y,y,y,y,n,n,n,n,n,y,n,? 284 | republican,y,n,n,y,y,y,n,n,n,y,n,?,y,y,n,n 285 | republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y 286 | democrat,n,n,y,n,n,y,y,y,y,y,y,n,n,n,?,y 287 | democrat,n,n,y,n,n,y,y,y,y,y,y,n,n,n,y,y 288 | democrat,n,n,y,n,n,y,?,y,?,y,y,y,n,y,y,? 289 | democrat,y,y,y,?,n,y,y,y,y,n,y,n,y,n,?,y 290 | 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /data/travel-times.csv: -------------------------------------------------------------------------------- 1 | Date,StartTime,DayOfWeek,GoingTo,Distance,MaxSpeed,AvgSpeed,AvgMovingSpeed,FuelEconomy,TotalTime,MovingTime,Take407All,Comments 2 | 1/6/2012,16:37,Friday,Home,51.29,127.4,78.3,84.8,,39.3,36.3,No, 3 | 1/6/2012,08:20,Friday,GSK,51.63,130.3,81.8,88.9,,37.9,34.9,No, 4 | 1/4/2012,16:17,Wednesday,Home,51.27,127.4,82.0,85.8,,37.5,35.9,No, 5 | 1/4/2012,07:53,Wednesday,GSK,49.17,132.3,74.2,82.9,,39.8,35.6,No, 6 | 1/3/2012,18:57,Tuesday,Home,51.15,136.2,83.4,88.1,,36.8,34.8,No, 7 | 1/3/2012,07:57,Tuesday,GSK,51.80,135.8,84.5,88.8,,36.8,35.0,No, 8 | 1/2/2012,17:31,Monday,Home,51.37,123.2,82.9,87.3,-,37.2,35.3,No, 9 | 1/2/2012,07:34,Monday,GSK,49.01,128.3,77.5,85.9,-,37.9,34.3,No, 10 | 12/23/2011,08:01,Friday,GSK,52.91,130.3,80.9,88.3,8.89,39.3,36.0,No, 11 | 12/22/2011,17:19,Thursday,Home,51.17,122.3,70.6,78.1,8.89,43.5,39.3,No, 12 | 12/22/2011,08:16,Thursday,GSK,49.15,129.4,74.0,81.4,8.89,39.8,36.2,No, 13 | 12/21/2011,07:45,Wednesday,GSK,51.77,124.8,71.7,78.9,8.89,43.3,39.4,No, 14 | 12/20/2011,16:05,Tuesday,Home,51.45,130.1,75.2,82.7,8.89,41.1,37.3,No, 15 | 12/20/2011,06:04,Tuesday,GSK,49.01,119.0,77.4,82.0,8.89,38.0,35.9,No, 16 | 12/19/2011,16:18,Monday,Home,51.04,132.2,77.5,83.5,8.89,39.5,36.7,No, 17 | 12/19/2011,07:34,Monday,GSK,52.00,137.8,76.5,87.8,8.89,40.8,35.5,No,Put snow tires on 18 | 12/16/2011,12:22,Friday,Home,51.05,128.4,86.9,90.6,9.08,35.2,33.8,No, 19 | 12/16/2011,07:21,Friday,GSK,49.04,124.6,71.1,80.2,9.08,41.4,36.7,No, 20 | 12/15/2011,16:14,Thursday,Home,51.06,126.9,80.5,84.9,9.08,38.1,36.1,No, 21 | 12/15/2011,07:19,Thursday,GSK,51.68,123.5,68.1,75.8,9.08,45.6,40.9,No, 22 | 12/14/2011,16:20,Wednesday,Home,51.04,123.4,75.1,79.4,9.08,40.8,38.6,No, 23 | 12/14/2011,07:23,Wednesday,GSK,51.67,123.5,76.6,82.7,9.08,40.5,37.5,No, 24 | 12/13/2011,17:43,Tuesday,Home,51.15,130.6,74.8,82.4,9.08,41.0,37.2,No, 25 | 12/13/2011,07:25,Tuesday,GSK,49.19,126.1,65.4,74.2,9.08,45.1,39.8,No, 26 | 12/12/2011,07:20,Monday,GSK,49.02,126.1,65.7,74.0,9.76,44.8,39.7,No, 27 | 12/9/2011,12:04,Friday,Home,51.14,126.8,87.3,90.2,9.76,35.2,34.0,No, 28 | 12/9/2011,07:22,Friday,GSK,51.69,128.4,74.0,77.3,9.76,41.9,40.1,No, 29 | 12/8/2011,17:41,Thursday,Home,51.07,125.0,74.6,81.5,9.76,41.1,37.6,No, 30 | 12/8/2011,07:14,Thursday,GSK,51.63,134.4,76.5,84.3,9.76,40.5,36.7,No, 31 | 12/7/2011,16:12,Wednesday,Home,51.10,126.5,79.9,85.6,9.76,38.4,35.8,No, 32 | 12/7/2011,07:18,Wednesday,GSK,51.64,124.6,73.6,82.0,9.76,42.1,37.8,No, 33 | 12/6/2011,17:24,Tuesday,Home,51.25,123.5,77.3,81.9,9.16,39.8,37.6,No, 34 | 12/6/2011,07:24,Tuesday,GSK,51.64,122.3,69.3,74.7,9.16,44.7,41.5,No, 35 | 12/5/2011,16:18,Monday,Home,50.18,124.0,71.0,79.5,9.16,42.4,37.9,No, 36 | 12/1/2011,16:15,Thursday,Home,51.55,129.6,74.2,83.7,,41.7,36.9,No, 37 | 12/1/2011,07:24,Thursday,GSK,51.38,124.6,80.1,84.4,,38.5,36.5,No, 38 | 11/30/2011,16:11,Wednesday,Home,51.09,128.7,71.5,76.7,,42.9,39.9,No, 39 | 11/30/2011,07:19,Wednesday,GSK,51.71,125.6,76.3,81.8,,40.7,37.9,No, 40 | 11/29/2011,16:52,Tuesday,Home,51.00,121.4,68.4,75.4,,44.7,40.6,No, 41 | 11/29/2011,07:23,Tuesday,GSK,51.74,112.2,55.3,61.0,,56.2,50.9,No,Heavy rain 42 | 11/28/2011,16:16,Monday,Home,51.05,128.2,72.4,78.8,,42.3,38.9,No, 43 | 11/28/2011,07:26,Monday,GSK,51.63,127.1,71.0,75.5,,43.6,41.1,No, 44 | 11/24/2011,16:15,Thursday,Home,51.49,126.6,74.0,82.8,9.3,41.8,37.3,No, 45 | 11/24/2011,07:23,Thursday,GSK,51.69,124.9,73.3,80.3,9.3,42.3,38.6,No, 46 | 11/23/2011,16:17,Wednesday,Home,60.32,129.4,68.9,74.6,9.3,52.5,48.5,No, 47 | 11/23/2011,07:22,Wednesday,GSK,51.60,126.4,67.3,73.6,9.3,46.0,42.1,No, 48 | 11/22/2011,16:15,Tuesday,Home,51.49,129.6,78.6,83.8,9.3,39.3,36.9,No, 49 | 11/22/2011,07:27,Tuesday,GSK,51.65,128.6,76.1,82.6,9.3,40.7,37.5,No, 50 | 11/21/2011,16:50,Monday,Home,51.31,123.1,60.9,67.2,10.05,50.6,45.8,No, 51 | 11/21/2011,07:24,Monday,GSK,52.25,127.3,38.1,50.3,10.05,82.3,62.4,No,Huge traffic backup 52 | 11/17/2011,16:16,Thursday,Home,51.16,127.6,72.4,77.4,10.05,42.4,39.6,No,Pumped tires up: check fuel economy improved? 53 | 11/17/2011,07:42,Thursday,GSK,51.67,127.0,70.9,77.7,10.05,43.7,39.9,No, 54 | 11/16/2011,16:13,Wednesday,Home,51.12,125.1,65.0,73.1,9.53,47.2,41.9,No,Backed up at Bronte 55 | 11/16/2011,07:26,Wednesday,GSK,51.70,129.8,73.4,80.4,9.53,42.3,38.6,No, 56 | 11/15/2011,17:36,Tuesday,Home,51.06,122.8,61.4,70.9,9.53,49.9,43.2,No,Backed up at Bronte 57 | 11/15/2011,07:21,Tuesday,GSK,51.50,127.6,70.5,77.7,9.53,43.8,39.8,No, 58 | 11/14/2011,16:15,Monday,Home,51.06,119.5,64.4,70.2,9.53,47.6,43.6,No, 59 | 11/14/2011,07:19,Monday,GSK,51.66,126.4,55.2,59.1,9.53,56.1,52.5,No, 60 | 11/10/2011,16:11,Thursday,Home,51.07,129.8,75.2,82.6,9.53,40.8,37.1,No, 61 | 11/10/2011,07:25,Thursday,GSK,51.68,132.5,69.8,74.7,9.53,44.4,41.5,No, 62 | 11/9/2011,16:15,Wednesday,Home,51.28,121.4,65.9,71.8,9.35,46.7,42.1,No,Rainy 63 | 11/9/2011,07:25,Wednesday,GSK,51.79,127.5,65.6,72.4,9.35,47.3,42.9,No, 64 | 11/8/2011,17:24,Tuesday,Home,50.75,131.3,89.5,93.5,9.35,34.3,32.6,Yes, 65 | 11/8/2011,07:25,Tuesday,GSK,51.82,126.8,66.1,73.9,9.35,47.0,42.1,No, 66 | 11/7/2011,16:05,Monday,Home,51.06,127.4,80.4,85.2,9.35,38.1,36.0,No, 67 | 11/7/2011,07:19,Monday,GSK,51.62,125.4,74.9,82.9,9.35,41.4,37.4,No, 68 | 11/4/2011,12:12,Friday,Home,51.03,128.4,82.2,89.7,9.35,37.2,34.1,No, 69 | 11/4/2011,08:16,Friday,GSK,51.81,130.2,82.1,85.0,9.35,37.9,36.6,No, 70 | 11/3/2011,17:00,Thursday,Home,51.07,130.3,75.8,81.7,8.32,40.4,37.5,No, 71 | 11/3/2011,07:46,Thursday,GSK,51.83,133.0,80.6,85.4,8.32,38.6,36.4,No, 72 | 11/2/2011,16:45,Wednesday,Home,51.27,121.5,75.1,79.1,8.32,41.0,38.9,No, 73 | 11/2/2011,07:51,Wednesday,GSK,51.83,129.3,72.5,78.1,8.32,42.9,39.8,No, 74 | 11/1/2011,08:13,Tuesday,GSK,51.74,129.7,71.8,79.4,8.32,43.2,39.1,No, 75 | 10/31/2011,15:49,Monday,Home,51.06,125.0,76.4,85.7,8.32,40.1,35.7,No, 76 | 10/31/2011,06:21,Monday,GSK,50.58,125.0,104.4,106.2,8.32,29.1,28.6,Yes, 77 | 10/28/2011,12:14,Friday,Home,51.28,120.5,83.0,89.5,8.32,37.1,34.4,No, 78 | 10/27/2011,08:20,Thursday,GSK,51.77,125.6,82.4,89.5,8.97,37.7,34.7,No, 79 | 10/26/2011,08:20,Wednesday,GSK,51.75,127.9,74.6,81.7,8.97,41.6,38.0,No, 80 | 10/25/2011,17:24,Tuesday,Home,52.87,123.5,65.1,72.4,8.97,48.7,43.8,No,"Rain, rain, rain" 81 | 10/25/2011,08:13,Tuesday,GSK,51.75,127.4,72.1,82.0,8.97,43.1,37.8,No, 82 | 10/24/2011,17:29,Monday,Home,51.06,126.5,71.6,78.6,8.97,42.8,39.0,No, 83 | 10/21/2011,08:31,Friday,GSK,50.64,129.0,106.6,112.1,8.97,28.5,27.1,Yes, 84 | 10/20/2011,17:37,Thursday,Home,51.33,125.2,72.5,79.6,8.75,42.5,38.7,No, 85 | 10/20/2011,08:22,Thursday,GSK,51.74,124.5,68.9,79.2,8.75,45.0,39.2,No, 86 | 10/19/2011,17:39,Wednesday,Home,51.30,126.2,63.4,73.3,8.75,48.6,42.0,No, 87 | 10/19/2011,08:24,Wednesday,GSK,51.68,130.2,75.7,81.6,8.75,41.0,38.0,No, 88 | 10/18/2011,17:28,Tuesday,Home,51.36,133.1,69.5,79.1,8.75,44.4,38.9,No, 89 | 10/18/2011,08:14,Tuesday,GSK,51.74,130.8,80.8,85.2,8.75,38.4,36.5,No, 90 | 10/17/2011,16:58,Monday,Home,51.30,127.3,78.6,82.9,8.75,39.1,37.1,No, 91 | 10/17/2011,08:22,Monday,GSK,50.61,137.1,93.7,100.3,8.75,32.4,30.3,Yes, 92 | 10/13/2011,08:36,Thursday,GSK,50.66,128.3,105.5,111.3,8.75,28.8,27.3,Yes, 93 | 10/12/2011,17:47,Wednesday,Home,51.40,114.4,59.7,65.8,8.75,51.7,46.9,No,"Rain, rain, rain" 94 | 10/12/2011,08:28,Wednesday,GSK,50.58,128.4,59.5,67.3,8.75,51.0,45.1,Yes,Accident: backup from Hamilton to 407 ramp 95 | 10/11/2011,17:17,Tuesday,Home,51.52,135.1,67.3,78.4,7.81,46.0,39.5,No, 96 | 10/11/2011,08:25,Tuesday,GSK,48.94,130.8,85.7,93.2,7.81,34.3,31.5,Yes, 97 | 10/7/2011,12:15,Friday,Home,51.02,124.8,80.4,88.9,7.97,38.1,34.4,Yes, 98 | 10/7/2011,08:23,Friday,GSK,50.68,128.3,101.9,107.2,7.97,29.9,28.4,Yes, 99 | 10/6/2011,17:58,Thursday,Home,51.29,126.0,73.3,76.3,7.97,42.0,40.3,No, 100 | 10/6/2011,08:22,Thursday,GSK,50.63,125.6,38.5,103.8,7.97,30.8,29.3,Yes, 101 | 10/5/2011,17:10,Wednesday,Home,51.31,127.5,69.5,75.5,7.97,44.3,40.8,No, 102 | 10/5/2011,08:36,Wednesday,GSK,50.59,128.5,103.4,108.0,7.97,29.4,28.1,Yes, 103 | 10/4/2011,17:39,Tuesday,Home,51.15,128.8,76.0,85.2,7.97,40.4,36.0,No, 104 | 10/4/2011,07:42,Tuesday,GSK,50.67,127.3,94.9,97.9,7.97,32.0,31.1,Yes, 105 | 10/3/2011,17:31,Monday,Home,51.22,126.7,81.2,86.4,7.97,37.8,35.6,No, 106 | 10/3/2011,07:41,Monday,GSK,50.65,127.4,91.1,95.2,7.97,33.4,31.9,Yes, 107 | 9/29/2011,17:17,Thursday,Home,51.39,128.4,65.3,71.9,8.93,47.2,42.9,No, 108 | 9/29/2011,08:22,Thursday,GSK,50.65,122.8,83.5,86.9,8.93,36.4,35.0,Yes, 109 | 9/28/2011,17:53,Wednesday,Home,51.23,124.9,79.2,81.5,8.93,38.8,37.7,No, 110 | 9/28/2011,07:57,Wednesday,GSK,50.65,128.8,102.2,105.7,8.93,29.7,28.7,Yes, 111 | 9/27/2011,17:26,Tuesday,Home,51.11,130.1,69.8,75.6,8.31,44.0,40.6,No, 112 | 9/27/2011,07:36,Tuesday,GSK,50.65,128.1,86.3,88.6,8.31,35.2,34.3,Yes,Raining 113 | 9/26/2011,17:37,Monday,Home,50.69,132.3,97.2,103.6,8.31,31.3,29.3,Yes, 114 | 9/26/2011,08:02,Monday,GSK,50.65,129.4,88.2,91.3,8.31,34.4,33.3,Yes, 115 | 9/22/2011,17:27,Thursday,Home,51.11,124.8,74.3,79.6,8.31,41.3,38.5,No, 116 | 9/22/2011,07:37,Thursday,GSK,50.67,130.0,97.6,101.6,8.31,31.1,29.9,Yes, 117 | 9/21/2011,18:05,Wednesday,Home,51.74,133.3,82.9,89.2,8.33,37.4,34.8,Yes, 118 | 9/21/2011,08:02,Wednesday,GSK,49.16,128.7,59.5,68.0,8.33,49.6,43.4,No, 119 | 9/20/2011,17:20,Tuesday,Home,51.23,127.2,71.7,76.9,8.33,42.9,40.0,No, 120 | 9/20/2011,08:10,Tuesday,GSK,51.69,128.7,70.4,70.1,8.33,44.1,39.7,No, 121 | 9/19/2011,16:43,Monday,Home,51.30,123.9,69.6,75.2,8.33,44.2,40.9,No, 122 | 9/19/2011,06:34,Monday,GSK,49.09,123.7,69.4,79.1,8.33,42.4,37.3,No, 123 | 9/15/2011,17:50,Thursday,Home,51.13,127.5,69.0,75.9,8.33,44.4,40.4,No, 124 | 9/15/2011,08:08,Thursday,GSK,50.67,129.3,99.0,103.3,8.33,30.7,29.4,Yes, 125 | 9/14/2011,17:36,Wednesday,Home,51.77,127.0,71.1,79.6,8.33,43.7,39.0,No, 126 | 9/14/2011,08:11,Wednesday,GSK,50.54,126.8,98.7,103.7,8.33,30.7,29.3,Yes, 127 | 9/13/2011,17:22,Tuesday,Home,51.58,134.3,70.0,80.0,8.5,44.2,38.7,No, 128 | 9/13/2011,07:51,Tuesday,GSK,49.12,129.1,52.7,63.2,8.5,55.9,46.6,No, 129 | 9/12/2011,17:04,Monday,Home,51.43,131.1,75.1,79.5,8.5,40.6,38.8,No, 130 | 9/12/2011,08:05,Monday,GSK,49.12,127.5,65.7,73.1,8.5,44.8,40.3,No, 131 | 9/8/2011,16:37,Thursday,Home,50.92,137.0,66.9,77.7,8.5,45.6,39.3,No, 132 | 9/8/2011,07:23,Thursday,GSK,49.25,124.7,53.5,63.1,8.5,55.2,46.9,No, 133 | 9/7/2011,17:24,Wednesday,Home,51.34,132.8,71.5,76.6,8.5,43.1,40.2,No, 134 | 9/7/2011,07:57,Wednesday,GSK,49.08,125.1,56.5,66.5,8.5,52.1,44.3,No,Back to school traffic? 135 | 9/6/2011,16:27,Tuesday,Home,52.88,131.6,95.4,98.3,8.5,33.3,32.3,Yes,Took 407 all the way (to McMaster) 136 | 9/6/2011,07:50,Tuesday,GSK,54.36,132.5,95.1,98.0,8.5,34.3,33.3,Yes, 137 | 9/2/2011,17:07,Friday,Home,51.17,129.7,77.7,87.9,8.5,39.5,34.9,No, 138 | 9/2/2011,07:39,Friday,GSK,51.65,132.7,82.6,91.4,8.5,37.5,33.9,No, 139 | 9/1/2011,17:33,Thursday,Home,52.42,128.9,81.6,88.0,8.5,38.5,35.7,No, 140 | 9/1/2011,08:08,Thursday,GSK,49.13,126.8,67.3,73.9,8.5,43.8,39.9,No, 141 | 8/31/2011,17:14,Wednesday,Home,51.15,140.9,71.5,79.5,8.5,42.9,38.6,No, 142 | 8/31/2011,08:09,Wednesday,GSK,49.07,127.7,65.9,77.3,8.5,44.7,38.1,No, 143 | 8/30/2011,17:38,Tuesday,Home,51.66,138.0,67.1,73.2,8.5,46.2,42.4,No, 144 | 8/30/2011,07:57,Tuesday,GSK,49.04,133.5,69.1,77.8,8.5,42.6,37.8,No, 145 | 8/29/2011,17:11,Monday,Home,51.04,131.0,75.5,84.1,8.5,40.5,36.4,No, 146 | 8/29/2011,07:48,Monday,GSK,49.15,124.0,63.9,74.0,8.5,46.1,39.9,No, 147 | 8/26/2011,16:29,Friday,Home,50.88,132.7,81.1,86.9,8.54,37.6,35.2,No, 148 | 8/26/2011,07:49,Friday,GSK,49.01,129.1,76.3,83.3,8.54,38.6,35.3,No, 149 | 8/25/2011,17:39,Thursday,Home,51.23,132.8,64.2,70.6,8.54,47.9,43.5,No, 150 | 8/25/2011,09:09,Thursday,GSK,50.45,133.5,106.8,111.1,8.54,28.3,27.3,Yes, 151 | 8/24/2011,16:47,Wednesday,Home,51.01,132.9,79.6,86.1,8.54,38.5,38.5,No, 152 | 8/24/2011,07:59,Wednesday,GSK,49.07,127.1,58.5,71.5,8.54,50.3,41.1,No,Heavy volume on Derry 153 | 8/23/2011,17:23,Tuesday,Home,51.22,129.7,79.7,84.5,8.54,38.5,36.4,No, 154 | 8/23/2011,07:54,Tuesday,GSK,49.20,124.5,71.0,76.7,8.54,41.6,38.5,No, 155 | 8/22/2011,16:44,Monday,Home,51.12,126.8,77.9,85.2,8.54,39.6,36.0,No, 156 | 8/22/2011,07:49,Monday,GSK,49.18,123.8,65.6,73.3,8.54,45.0,40.2,No, 157 | 8/19/2011,17:42,Friday,Home,51.09,126.5,78.1,81.6,8.37,39.3,37.6,No, 158 | 8/19/2011,07:05,Friday,GSK,49.18,123.0,72.0,81.4,8.37,41.0,36.3,No,Start early to run a batch 159 | 8/18/2011,17:05,Thursday,Home,50.97,129.6,66.6,76.2,8.37,45.9,40.2,No, 160 | 8/18/2011,08:11,Thursday,GSK,52.26,137.7,51.2,64.1,8.37,61.2,48.9,No,Accident at 403/highway 6; detour along Dundas 161 | 8/17/2011,17:17,Wednesday,Home,51.11,129.8,68.9,75.7,8.37,44.5,40.5,No, 162 | 8/17/2011,08:10,Wednesday,GSK,49.10,124.8,67.9,76.2,8.37,43.4,38.7,No, 163 | 8/16/2011,17:27,Tuesday,Home,51.14,133.4,82.4,87.0,8.37,37.2,35.3,No, 164 | 8/16/2011,08:15,Tuesday,GSK,49.14,131.7,75.0,80.2,8.37,39.3,36.7,No, 165 | 8/15/2011,17:38,Monday,Home,51.11,132.3,78.0,83.7,8.37,39.3,36.7,No, 166 | 8/15/2011,08:15,Monday,GSK,49.20,124.7,71.3,75.7,8.37,41.4,39.0,No, 167 | 8/12/2011,17:25,Friday,Home,55.57,127.7,69.6,77.1,8.54,47.9,43.2,No,Detour taken 168 | 8/12/2011,08:05,Friday,GSK,49.02,128.4,76.7,82.9,8.54,38.4,35.5,No,Must be Friday 169 | 8/11/2011,17:25,Thursday,Home,51.18,133.6,80.7,86.0,8.54,38.0,35.7,No, 170 | 8/11/2011,08:13,Thursday,GSK,48.99,125.5,68.3,79.5,8.54,43.0,37.0,No, 171 | 8/10/2011,17:14,Wednesday,Home,51.03,127.4,73.5,79.3,8.54,41.7,38.6,No, 172 | 8/10/2011,08:13,Wednesday,GSK,48.98,124.8,72.8,78.8,8.54,40.4,37.3,No, 173 | 8/9/2011,17:13,Tuesday,Home,51.15,127.1,72.1,82.3,8.54,42.5,37.3,No, 174 | 8/9/2011,08:15,Tuesday,GSK,49.08,134.8,60.5,67.2,8.54,48.7,43.8,No,Medium amount of rain 175 | 8/8/2011,17:05,Monday,Home,52.35,127.5,76.9,84.2,8.54,40.9,37.3,No, 176 | 8/8/2011,08:07,Monday,GSK,49.25,126.3,68.5,78.2,8.54,43.1,37.8,No,New tires 177 | 8/5/2011,17:00,Friday,Home,51.94,126.7,74.5,82.6,8.48,41.9,37.7,No, 178 | 8/5/2011,08:20,Friday,GSK,49.13,123.9,74.1,79.9,8.48,39.8,36.9,No, 179 | 8/4/2011,17:38,Thursday,Home,50.96,131.9,70.3,78.5,8.48,43.5,38.9,No, 180 | 8/4/2011,08:17,Thursday,GSK,49.12,122.4,71.5,77.3,8.48,41.2,38.2,No, 181 | 8/3/2011,17:14,Wednesday,Home,51.64,125.0,72.2,78.7,8.48,42.9,39.4,No, 182 | 8/3/2011,08:06,Wednesday,GSK,49.06,121.9,71.5,78.7,8.48,41.2,37.4,No, 183 | 8/2/2011,17:22,Tuesday,Home,51.16,124.2,76.3,83.2,8.48,40.2,36.9,No, 184 | 8/2/2011,07:38,Tuesday,GSK,53.48,124.9,68.8,78.8,8.48,46.7,40.7,No,Turn around on Derry 185 | 7/29/2011,20:31,Friday,Home,50.68,135.6,107.7,110.4,8.45,28.2,27.6,Yes, 186 | 7/29/2011,08:22,Friday,GSK,49.07,121.1,73.2,77.7,8.45,40.2,37.9,No,Empty roads 187 | 7/28/2011,17:46,Thursday,Home,51.09,128.5,76.0,84.8,8.45,40.3,36.2,No, 188 | 7/28/2011,08:11,Thursday,GSK,49.11,120.1,69.1,73.1,8.45,42.6,40.3,No, 189 | 7/27/2011,17:24,Wednesday,Home,50.98,124.9,68.3,71.9,8.45,44.8,42.6,No,Police slowdown on 403 190 | 7/27/2011,08:15,Wednesday,GSK,48.82,124.5,70.4,77.8,8.45,41.6,37.6,No, 191 | 7/26/2011,17:15,Tuesday,Home,51.28,122.1,43.7,51.5,8.45,70.5,59.8,No,Accident blocked 407 exit 192 | 7/26/2011,08:11,Tuesday,GSK,49.16,122.6,71.9,76.8,8.45,41.0,38.4,No, 193 | 7/25/2011,16:59,Monday,Home,51.05,126.6,70.4,78.8,8.45,51.1,38.9,No, 194 | 7/25/2011,08:06,Monday,GSK,48.32,121.2,63.4,78.4,8.45,45.7,37.0,No, 195 | 7/22/2011,16:47,Friday,Home,51.24,126.3,75.8,81.8,8.28,40.6,37.6,No, 196 | 7/22/2011,08:28,Friday,GSK,51.05,123.3,88.9,96.7,8.28,34.5,31.7,Yes, 197 | 7/21/2011,07:59,Thursday,GSK,48.35,129.3,81.5,89.0,8.28,35.6,32.6,Yes, 198 | 7/20/2011,17:17,Wednesday,Home,53.47,124.0,58.6,71.0,7.89,54.8,45.2,No, 199 | 7/20/2011,08:24,Wednesday,GSK,48.50,125.8,75.7,87.3,7.89,38.5,33.3,Yes, 200 | 7/19/2011,17:17,Tuesday,Home,51.16,126.7,92.2,102.6,7.89,33.3,29.9,Yes, 201 | 7/19/2011,08:11,Tuesday,GSK,50.96,124.3,82.3,96.4,7.89,37.2,31.7,Yes, 202 | 7/18/2011,08:09,Monday,GSK,54.52,125.6,49.9,82.4,7.89,65.5,39.7,No, 203 | 7/14/2011,08:03,Thursday,GSK,50.90,123.7,76.2,95.1,7.89,40.1,32.1,Yes, 204 | 7/13/2011,17:08,Wednesday,Home,51.96,132.6,57.5,76.7,,54.2,40.6,Yes, 205 | 7/12/2011,17:51,Tuesday,Home,53.28,125.8,61.6,87.6,,51.9,36.5,Yes, 206 | 7/11/2011,16:56,Monday,Home,51.73,125.0,62.8,92.5,,49.5,33.6,Yes, -------------------------------------------------------------------------------- /extra_credit_roc.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# ROC Curve\n", 8 | "\n", 9 | "Logistic Regression doesn't just predict which class a datapoint belongs to, it actually gives a probability. This is a value from 0 to 1 which indicates the probability of it belonging to class 1. We typically say if the value is 0.5 or above it belongs in class 1, and if the value is below 0.5 we put it in class 0.\n", 10 | "\n", 11 | "However, we can vary this threshold which will affect the precision and recall values. Varying the threshold gives us an infinite number of models. We plot two values, one of which is the recall. The other is the false positive rate, which catches the same idea as the precision.\n", 12 | "\n", 13 | "Here are the two values (and their many names):\n", 14 | "\n", 15 | "* **True Positive Rate (Sensitivity, Recall)**\n", 16 | "\n", 17 | " ```\n", 18 | " number predicted positively that are truely positive\n", 19 | " ------------------------------------------------------\n", 20 | " number truely positive\n", 21 | " ```\n", 22 | "\n", 23 | "* **False Positive Rate (1 - Specificity)**\n", 24 | "\n", 25 | " ```\n", 26 | " number predicted positively that are incorrect\n", 27 | " ------------------------------------------------\n", 28 | " number truely negative\n", 29 | " ```" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "Follow the [sklearn's example](http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html) to build an ROC curve for one or both of the example datasets we've used for classification." 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": { 43 | "collapsed": false 44 | }, 45 | "outputs": [], 46 | "source": [] 47 | } 48 | ], 49 | "metadata": { 50 | "kernelspec": { 51 | "display_name": "Python 2", 52 | "language": "python", 53 | "name": "python2" 54 | }, 55 | "language_info": { 56 | "codemirror_mode": { 57 | "name": "ipython", 58 | "version": 2 59 | }, 60 | "file_extension": ".py", 61 | "mimetype": "text/x-python", 62 | "name": "python", 63 | "nbconvert_exporter": "python", 64 | "pygments_lexer": "ipython2", 65 | "version": "2.7.9" 66 | } 67 | }, 68 | "nbformat": 4, 69 | "nbformat_minor": 0 70 | } 71 | -------------------------------------------------------------------------------- /images/classification.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/classification.png -------------------------------------------------------------------------------- /images/dtree.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/dtree.gif -------------------------------------------------------------------------------- /images/knn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/knn.png -------------------------------------------------------------------------------- /images/linear_regression.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/linear_regression.png -------------------------------------------------------------------------------- /images/linear_regression_line.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/linear_regression_line.png -------------------------------------------------------------------------------- /images/logistic_regression.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GalvanizeDataScience/ml-workshop/3517b067211423dc559eeb3bb2dd6560094c62fb/images/logistic_regression.png -------------------------------------------------------------------------------- /regression_exercise.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Travel times\n", 8 | "##Description:\t\n", 9 | "A driver uses an app to track GPS coordinates as he drives to work and back each day. The app collects the location and elevation data. Data for about 200 trips are summarized in this data set.\n", 10 | "##Data source:\t\n", 11 | "* Date of travel\n", 12 | "* StartTime: when getting into the car\n", 13 | "* DayOfWeek: the day name\n", 14 | "* GoingTo: direction of travel\n", 15 | "* Distance travelled in kilometers\n", 16 | "* MaxSpeed: fastest speed recorded (all trips are on the 407 highway for some portion)\n", 17 | "* AvgSpeed: the average speed for the entire trip\n", 18 | "* AvgMovingSpeed: the average speed recorded only while the car is moving\n", 19 | "* FuelEconomy: a rough estimate of fuel economy (it is inaccurate)\n", 20 | "* TotalTime: duration of the entire trip, in minutes\n", 21 | "* MovingTime: duration when the car was considered to be moving (i.e. not counting traffic delays, accidents, or time while the car is stationary)\n", 22 | "* Take407All: is Yes if the 407 toll highway was taken for the entire trip. I try to avoid taking the 407, taking slower back routes to save costs. But some days I'm running late, or just lazy, and take it all the way.\n", 23 | "\n", 24 | "##Comments\n", 25 | "* Data shape:\t205 rows and 13 columns\n", 26 | "* Usage restrictions:\tNone\n", 27 | "* Contact person:\tKevin Dunn\n", 28 | "* Contact details:\tdatasets@connectmv.com\n", 29 | "* Added here on:\t17 September 2015\n", 30 | "* Last updated:\t09 January 2012 13:21\n", 31 | "\n", 32 | "#Task\n", 33 | "\n", 34 | "Build a regression model to predict the total travel time.\n", 35 | "\n", 36 | "##Extension\n", 37 | "Many of the data points in this data set are only available _after_ the drive. But what if we want to predict how long it will take _before_ we leave?\n", 38 | "\n", 39 | "Build a model that makes a prediction based only on data available to the driver _before_ they leave?" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 1, 45 | "metadata": { 46 | "collapsed": false 47 | }, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "Populating the interactive namespace from numpy and matplotlib\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "import pandas as pd\n", 59 | "import numpy as np\n", 60 | "import matplotlib.pyplot as plt\n", 61 | "%pylab inline" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "##1. Read the data into a data frame using pandas." 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "metadata": { 75 | "collapsed": false 76 | }, 77 | "outputs": [], 78 | "source": [] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "##2. Create X and y matrices\n", 85 | "\n", 86 | "Take only the columns which have numeric values and make that the `X` matrix. Make the `TotalTime` the `y` vector. Make sure that the Total Time *isn't* in your X matrix!" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": { 93 | "collapsed": false 94 | }, 95 | "outputs": [], 96 | "source": [] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "##3. Build a baseline KNN model with k=10" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 2, 108 | "metadata": { 109 | "collapsed": false 110 | }, 111 | "outputs": [], 112 | "source": [ 113 | "from sklearn.neighbors import KNeighborsRegressor\n", 114 | "\n" 115 | ] 116 | }, 117 | { 118 | "cell_type": "markdown", 119 | "metadata": {}, 120 | "source": [ 121 | "## 4. Calculate the root mean squared error of the baseline." 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 3, 127 | "metadata": { 128 | "collapsed": false 129 | }, 130 | "outputs": [], 131 | "source": [ 132 | "from sklearn.metrics import mean_squared_error\n", 133 | "\n" 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": {}, 139 | "source": [ 140 | "## Challenge 1: Use Cross-Validation to compare performance of different values of k." 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 4, 146 | "metadata": { 147 | "collapsed": false 148 | }, 149 | "outputs": [], 150 | "source": [ 151 | "from sklearn.cross_validation import KFold\n", 152 | "\n", 153 | "# function from lecture notes\n", 154 | "def kfold_cv_error(model, X, y, folds):\n", 155 | " test_error = []\n", 156 | " for train_index, test_index in folds:\n", 157 | " model.fit(X[train_index], y[train_index])\n", 158 | " predict = model.predict(X[test_index])\n", 159 | " fold_error = np.sqrt(mean_squared_error(y[test_index], predict))\n", 160 | " test_error.append(fold_error)\n", 161 | " return np.mean(test_error)\n", 162 | "\n" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": { 168 | "collapsed": true 169 | }, 170 | "source": [ 171 | "## Challenge 2: Transform non-numeric data\n", 172 | "\n", 173 | "* Make the `Take407All` variable a boolean column.\n", 174 | "* Make the `GoingTo` column a boolean column.\n", 175 | "* Make the pandas function `get_dummies` to create dummy columns for the DayOfWeek" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": null, 181 | "metadata": { 182 | "collapsed": false 183 | }, 184 | "outputs": [], 185 | "source": [] 186 | } 187 | ], 188 | "metadata": { 189 | "kernelspec": { 190 | "display_name": "Python 2", 191 | "language": "python", 192 | "name": "python2" 193 | }, 194 | "language_info": { 195 | "codemirror_mode": { 196 | "name": "ipython", 197 | "version": 2 198 | }, 199 | "file_extension": ".py", 200 | "mimetype": "text/x-python", 201 | "name": "python", 202 | "nbconvert_exporter": "python", 203 | "pygments_lexer": "ipython2", 204 | "version": "2.7.9" 205 | } 206 | }, 207 | "nbformat": 4, 208 | "nbformat_minor": 0 209 | } 210 | -------------------------------------------------------------------------------- /regression_exercise_soln.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Travel times\n", 8 | "##Description:\t\n", 9 | "A driver uses an app to track GPS coordinates as he drives to work and back each day. The app collects the location and elevation data. Data for about 200 trips are summarized in this data set.\n", 10 | "##Data source:\t\n", 11 | "* Date of travel\n", 12 | "* StartTime: when getting into the car\n", 13 | "* DayOfWeek: the day name\n", 14 | "* GoingTo: direction of travel\n", 15 | "* Distance travelled in kilometers\n", 16 | "* MaxSpeed: fastest speed recorded (all trips are on the 407 highway for some portion)\n", 17 | "* AvgSpeed: the average speed for the entire trip\n", 18 | "* AvgMovingSpeed: the average speed recorded only while the car is moving\n", 19 | "* FuelEconomy: a rough estimate of fuel economy (it is inaccurate)\n", 20 | "* TotalTime: duration of the entire trip, in minutes\n", 21 | "* MovingTime: duration when the car was considered to be moving (i.e. not counting traffic delays, accidents, or time while the car is stationary)\n", 22 | "* Take407All: is Yes if the 407 toll highway was taken for the entire trip. I try to avoid taking the 407, taking slower back routes to save costs. But some days I'm running late, or just lazy, and take it all the way.\n", 23 | "\n", 24 | "##Comments\n", 25 | "* Data shape:\t205 rows and 13 columns\n", 26 | "* Usage restrictions:\tNone\n", 27 | "* Contact person:\tKevin Dunn\n", 28 | "* Contact details:\tdatasets@connectmv.com\n", 29 | "* Added here on:\t17 September 2015\n", 30 | "* Last updated:\t09 January 2012 13:21\n", 31 | "\n", 32 | "#Task\n", 33 | "\n", 34 | "Build a regression model to predict the total travel time.\n", 35 | "\n", 36 | "##Extension\n", 37 | "Many of the data points in this data set are only available _after_ the drive. But what if we want to predict how long it will take _before_ we leave?\n", 38 | "\n", 39 | "Build a model that makes a prediction based only on data available to the driver _before_ they leave?" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 1, 45 | "metadata": { 46 | "collapsed": false 47 | }, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "Populating the interactive namespace from numpy and matplotlib\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "import pandas as pd\n", 59 | "import numpy as np\n", 60 | "import matplotlib.pyplot as plt\n", 61 | "%pylab inline" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "##1. Read the data into a data frame using pandas." 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 2, 74 | "metadata": { 75 | "collapsed": false 76 | }, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/html": [ 81 | "
\n", 82 | "\n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | "
DateStartTimeDayOfWeekGoingToDistanceMaxSpeedAvgSpeedAvgMovingSpeedFuelEconomyTotalTimeMovingTimeTake407AllComments
0 1/6/2012 16:37 Friday Home 51.29 127.4 78.3 84.8 NaN 39.3 36.3 No NaN
1 1/6/2012 08:20 Friday GSK 51.63 130.3 81.8 88.9 NaN 37.9 34.9 No NaN
2 1/4/2012 16:17 Wednesday Home 51.27 127.4 82.0 85.8 NaN 37.5 35.9 No NaN
3 1/4/2012 07:53 Wednesday GSK 49.17 132.3 74.2 82.9 NaN 39.8 35.6 No NaN
4 1/3/2012 18:57 Tuesday Home 51.15 136.2 83.4 88.1 NaN 36.8 34.8 No NaN
\n", 184 | "
" 185 | ], 186 | "text/plain": [ 187 | " Date StartTime DayOfWeek GoingTo Distance MaxSpeed AvgSpeed \\\n", 188 | "0 1/6/2012 16:37 Friday Home 51.29 127.4 78.3 \n", 189 | "1 1/6/2012 08:20 Friday GSK 51.63 130.3 81.8 \n", 190 | "2 1/4/2012 16:17 Wednesday Home 51.27 127.4 82.0 \n", 191 | "3 1/4/2012 07:53 Wednesday GSK 49.17 132.3 74.2 \n", 192 | "4 1/3/2012 18:57 Tuesday Home 51.15 136.2 83.4 \n", 193 | "\n", 194 | " AvgMovingSpeed FuelEconomy TotalTime MovingTime Take407All Comments \n", 195 | "0 84.8 NaN 39.3 36.3 No NaN \n", 196 | "1 88.9 NaN 37.9 34.9 No NaN \n", 197 | "2 85.8 NaN 37.5 35.9 No NaN \n", 198 | "3 82.9 NaN 39.8 35.6 No NaN \n", 199 | "4 88.1 NaN 36.8 34.8 No NaN " 200 | ] 201 | }, 202 | "execution_count": 2, 203 | "metadata": {}, 204 | "output_type": "execute_result" 205 | } 206 | ], 207 | "source": [ 208 | "df = pd.read_csv('data/travel-times.csv')\n", 209 | "df.head()" 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "##2. Create X and y matrices\n", 217 | "\n", 218 | "Take only the columns which have numeric values and make that the `X` matrix. Make the `TotalTime` the `y` vector. Make sure that the Total Time *isn't* in your X matrix!" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 3, 224 | "metadata": { 225 | "collapsed": false 226 | }, 227 | "outputs": [], 228 | "source": [ 229 | "regression_columns = ['Distance', 'MaxSpeed', 'AvgSpeed', 'AvgMovingSpeed', 'MovingTime']\n", 230 | "X = df[regression_columns].values\n", 231 | "y = df['TotalTime'].values" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "##3. Build a baseline KNN model with k=10" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 4, 244 | "metadata": { 245 | "collapsed": false 246 | }, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "text/plain": [ 251 | "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", 252 | " metric_params=None, n_neighbors=10, p=2, weights='uniform')" 253 | ] 254 | }, 255 | "execution_count": 4, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "from sklearn.neighbors import KNeighborsRegressor\n", 262 | "\n", 263 | "baseline = KNeighborsRegressor(n_neighbors=10)\n", 264 | "baseline.fit(X, y)" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": {}, 270 | "source": [ 271 | "## 4. Calculate the root mean squared error of the baseline." 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 5, 277 | "metadata": { 278 | "collapsed": false 279 | }, 280 | "outputs": [ 281 | { 282 | "data": { 283 | "text/plain": [ 284 | "2.7731230083629885" 285 | ] 286 | }, 287 | "execution_count": 5, 288 | "metadata": {}, 289 | "output_type": "execute_result" 290 | } 291 | ], 292 | "source": [ 293 | "from sklearn.metrics import mean_squared_error\n", 294 | "\n", 295 | "predict = baseline.predict(X)\n", 296 | "np.sqrt(mean_squared_error(predict, df.TotalTime))" 297 | ] 298 | }, 299 | { 300 | "cell_type": "markdown", 301 | "metadata": {}, 302 | "source": [ 303 | "## Challenge 1: Use Cross-Validation to compare performance of different values of k." 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": 6, 309 | "metadata": { 310 | "collapsed": false 311 | }, 312 | "outputs": [ 313 | { 314 | "data": { 315 | "text/plain": [ 316 | "[]" 317 | ] 318 | }, 319 | "execution_count": 6, 320 | "metadata": {}, 321 | "output_type": "execute_result" 322 | }, 323 | { 324 | "data": { 325 | "image/png": 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326 | "text/plain": [ 327 | "" 328 | ] 329 | }, 330 | "metadata": {}, 331 | "output_type": "display_data" 332 | } 333 | ], 334 | "source": [ 335 | "from sklearn.cross_validation import KFold\n", 336 | "\n", 337 | "# function from lecture notes\n", 338 | "def kfold_cv_error(model, X, y, folds):\n", 339 | " test_error = []\n", 340 | " for train_index, test_index in folds:\n", 341 | " model.fit(X[train_index], y[train_index])\n", 342 | " predict = model.predict(X[test_index])\n", 343 | " fold_error = np.sqrt(mean_squared_error(y[test_index], predict))\n", 344 | " test_error.append(fold_error)\n", 345 | " return np.mean(test_error)\n", 346 | "\n", 347 | "#What values of k to test.\n", 348 | "neighbors = range(1, 20)\n", 349 | "#What errors for those k\n", 350 | "folds = KFold(df.shape[0], n_folds=10)\n", 351 | "errors = [kfold_cv_error(KNeighborsRegressor(k), X, y, folds) for k in neighbors]\n", 352 | "\n", 353 | "plt.plot(neighbors, errors)" 354 | ] 355 | }, 356 | { 357 | "cell_type": "markdown", 358 | "metadata": { 359 | "collapsed": true 360 | }, 361 | "source": [ 362 | "## Challenge 2: Transform non-numeric data\n", 363 | "\n", 364 | "* Make the `Take407All` variable a boolean column.\n", 365 | "* Make the `GoingTo` column a boolean column.\n", 366 | "* Make the pandas function `get_dummies` to create dummy columns for the DayOfWeek" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": 7, 372 | "metadata": { 373 | "collapsed": false 374 | }, 375 | "outputs": [ 376 | { 377 | "data": { 378 | "text/html": [ 379 | "
\n", 380 | "\n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | "
DistanceMaxSpeedAvgSpeedAvgMovingSpeedMovingTimeFridayMondayThursdayTuesdayWednesday
0 51.29 127.4 78.3 84.8 36.3 1 0 0 0 0
1 51.63 130.3 81.8 88.9 34.9 1 0 0 0 0
2 51.27 127.4 82.0 85.8 35.9 0 0 0 0 1
3 49.17 132.3 74.2 82.9 35.6 0 0 0 0 1
4 51.15 136.2 83.4 88.1 34.8 0 0 0 1 0
\n", 464 | "
" 465 | ], 466 | "text/plain": [ 467 | " Distance MaxSpeed AvgSpeed AvgMovingSpeed MovingTime Friday Monday \\\n", 468 | "0 51.29 127.4 78.3 84.8 36.3 1 0 \n", 469 | "1 51.63 130.3 81.8 88.9 34.9 1 0 \n", 470 | "2 51.27 127.4 82.0 85.8 35.9 0 0 \n", 471 | "3 49.17 132.3 74.2 82.9 35.6 0 0 \n", 472 | "4 51.15 136.2 83.4 88.1 34.8 0 0 \n", 473 | "\n", 474 | " Thursday Tuesday Wednesday \n", 475 | "0 0 0 0 \n", 476 | "1 0 0 0 \n", 477 | "2 0 0 1 \n", 478 | "3 0 0 1 \n", 479 | "4 0 1 0 " 480 | ] 481 | }, 482 | "execution_count": 7, 483 | "metadata": {}, 484 | "output_type": "execute_result" 485 | } 486 | ], 487 | "source": [ 488 | "df['Take407All_bool'] = df['Take407All'] == 'Yes'\n", 489 | "df['GoingHome_bool'] = df['GoingTo'] == 'Home'\n", 490 | "columns = regression_columns + ['Take407All_bool', 'GoingHome_bool']\n", 491 | "tt_transformed = pd.concat([df[regression_columns],\n", 492 | " pd.get_dummies(df['DayOfWeek'])], axis=1)\n", 493 | "tt_transformed.head()" 494 | ] 495 | }, 496 | { 497 | "cell_type": "code", 498 | "execution_count": 8, 499 | "metadata": { 500 | "collapsed": false 501 | }, 502 | "outputs": [ 503 | { 504 | "data": { 505 | "text/plain": [ 506 | "[]" 507 | ] 508 | }, 509 | "execution_count": 8, 510 | "metadata": {}, 511 | "output_type": "execute_result" 512 | }, 513 | { 514 | "data": { 515 | "image/png": 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516 | "text/plain": [ 517 | "" 518 | ] 519 | }, 520 | "metadata": {}, 521 | "output_type": "display_data" 522 | } 523 | ], 524 | "source": [ 525 | "X_transformed = tt_transformed.values\n", 526 | "\n", 527 | "neighbors = range(1, 20)\n", 528 | "folds = KFold(df.shape[0], n_folds=10)\n", 529 | "errors = [kfold_cv_error(KNeighborsRegressor(k), X_transformed, y, folds) for k in neighbors]\n", 530 | "\n", 531 | "plt.plot(neighbors, errors)" 532 | ] 533 | } 534 | ], 535 | "metadata": { 536 | "kernelspec": { 537 | "display_name": "Python 2", 538 | "language": "python", 539 | "name": "python2" 540 | }, 541 | "language_info": { 542 | "codemirror_mode": { 543 | "name": "ipython", 544 | "version": 2 545 | }, 546 | "file_extension": ".py", 547 | "mimetype": "text/x-python", 548 | "name": "python", 549 | "nbconvert_exporter": "python", 550 | "pygments_lexer": "ipython2", 551 | "version": "2.7.9" 552 | } 553 | }, 554 | "nbformat": 4, 555 | "nbformat_minor": 0 556 | } 557 | --------------------------------------------------------------------------------