diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d11f34b --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +*.pyc +.ipynb_checkpoints/ diff --git a/data_exploration.ipynb b/data_exploration.ipynb new file mode 100644 index 0000000..0a74b39 --- /dev/null +++ b/data_exploration.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
4503Allen, Mr. William Henrymale35003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", + "2 Heikkinen, Miss. Laina female 26 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", + "4 Allen, Mr. William Henry male 35 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv('train.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper function for calculating survival rate" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def getRate(group):\n", + " \"\"\"\n", + " Given a group of passengers, return the survival rate\n", + " \"\"\"\n", + " if len(group):\n", + " return sum(group.Survived) / float(len(group))\n", + " else:\n", + " return 0.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at individual features" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Gender:\n", + "Males (577):\t0.188908145581\n", + "Females (314):\t0.742038216561\n" + ] + } + ], + "source": [ + "males = df[df.Sex == 'male']\n", + "females = df[df.Sex == 'female']\n", + "\n", + "print 'Survival Rates by Gender:'\n", + "print 'Males ({}):\\t'.format(len(males)), getRate(males)\n", + "print 'Females ({}):\\t'.format(len(females)), getRate(females)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I was shocked to see that males had a survival rate of 0.19 while females had a survival rate of 0.74. I was expecting females to have a higher survival rate because of the saying 'women and children first,' but not by this much." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Passenger Class:\n", + "Upper (216):\t0.62962962963\n", + "Middle (184):\t0.472826086957\n", + "Lower (491):\t0.242362525458\n" + ] + } + ], + "source": [ + "upper = df[df.Pclass == 1]\n", + "middle = df[df.Pclass == 2]\n", + "lower = df[df.Pclass == 3]\n", + "\n", + "print 'Survival Rates by Passenger Class:'\n", + "print 'Upper ({}):\\t'.format(len(upper)), getRate(upper)\n", + "print 'Middle ({}):\\t'.format(len(middle)), getRate(middle)\n", + "print 'Lower ({}):\\t'.format(len(lower)), getRate(lower)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upper class passengers had a survival rate of 0.63, middle class passengers had a survival rate of 0.47, and lower class passengers had a survival rate of 0.24. This is not very surprising since I would guess that the upper class passengers would have cabins located higher up on the ship. It is also possible that they were given preferential treatment in the evacuation process." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Age:\n", + "Unknown (177):\t0.293785310734\n", + "Infants (24):\t0.625\n", + "Children (89):\t0.516853932584\n", + "Adults (601):\t0.381031613977\n" + ] + } + ], + "source": [ + "unknown = df[df.Age.isnull()]\n", + "infants = df[df.Age <= 2]\n", + "children = df[(df.Age > 2) & (df.Age < 18)]\n", + "adults = df[df.Age >= 18]\n", + "\n", + "print 'Survival Rates by Age:'\n", + "print 'Unknown ({}):\\t'.format(len(unknown)), getRate(unknown)\n", + "print 'Infants ({}):\\t'.format(len(infants)), getRate(infants)\n", + "print 'Children ({}):\\t'.format(len(children)), getRate(children)\n", + "print 'Adults ({}):\\t'.format(len(adults)), getRate(adults)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike with the previous categories, there were some passengers of unknown age. They had a survival rate of 0.29, infants (less than two years old) had a survival rate of 0.63, children (two to 18 years old) had a survival rate of 0.52, and adults (18 years or older) had a survival rate of 0.38. The higher survival rate in infants and children is unsurprising for the same reason as the higher survival rate in females." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Port of Embarkation:\n", + "Unknown (2):\t\t1.0\n", + "Cherbourg (168):\t0.553571428571\n", + "Queenstown (77):\t0.38961038961\n", + "Southampton (644):\t0.336956521739\n" + ] + } + ], + "source": [ + "unknown = df[df.Embarked.isnull()]\n", + "cherbourg = df[df.Embarked == 'C']\n", + "queenstown = df[df.Embarked == 'Q']\n", + "southampton = df[df.Embarked == 'S']\n", + "\n", + "print 'Survival Rates by Port of Embarkation:'\n", + "print 'Unknown ({}):\\t\\t'.format(len(unknown)), getRate(unknown)\n", + "print 'Cherbourg ({}):\\t'.format(len(cherbourg)), getRate(cherbourg)\n", + "print 'Queenstown ({}):\\t'.format(len(queenstown)), getRate(queenstown)\n", + "print 'Southampton ({}):\\t'.format(len(southampton)), getRate(southampton)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I was not expecting the port of embarkation to have much of an effect on survival rate, but it does look like those who boarded in Cherbourg had a higher survival rate (0.55) than those who boarded in Queenstown or Southampton (0.39 and 0.34, respectively). My guess is that Cherbourg had a higher percentage of women, children, or upper class passengers than the other locations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at combinations of features" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Passenger Class and Gender:\n", + "Upper Males (122):\t0.368852459016\n", + "Upper Females (94):\t0.968085106383\n", + "Middle Males (108):\t0.157407407407\n", + "Middle Females (76):\t0.921052631579\n", + "Lower Males (347):\t0.135446685879\n", + "Lower Females (144):\t0.5\n" + ] + } + ], + "source": [ + "upper_males = df[(df.Pclass == 1) & (df.Sex == 'male')]\n", + "upper_females = df[(df.Pclass == 1) & (df.Sex == 'female')]\n", + "middle_males = df[(df.Pclass == 2) & (df.Sex == 'male')]\n", + "middle_females = df[(df.Pclass == 2) & (df.Sex == 'female')]\n", + "lower_males = df[(df.Pclass == 3) & (df.Sex == 'male')]\n", + "lower_females = df[(df.Pclass == 3) & (df.Sex == 'female')]\n", + "\n", + "print 'Survival Rates by Passenger Class and Gender:'\n", + "print 'Upper Males ({}):\\t'.format(len(upper_males)), getRate(upper_males)\n", + "print 'Upper Females ({}):\\t'.format(len(upper_females)), getRate(upper_females)\n", + "print 'Middle Males ({}):\\t'.format(len(middle_males)), getRate(middle_males)\n", + "print 'Middle Females ({}):\\t'.format(len(middle_females)), getRate(middle_females)\n", + "print 'Lower Males ({}):\\t'.format(len(lower_males)), getRate(lower_males)\n", + "print 'Lower Females ({}):\\t'.format(len(lower_females)), getRate(lower_females)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This makes sense; within a passenger class, the females had the higher rate of survival, and within a gender, the upper class had the highest rate of survival. It is a little surprising to me how drastic the difference is between the classes. Upper and middle class females had survival rates of over 0.92 while lower class females had a survival rate of only 0.50. For males, the middle class swung the other way. Upper class males had a survival rate of 0.37 while middle and lower class males had survival rates of less than 0.16. In both cases, one or two groups had approximately double the survival rate of the other(s), but the middle class was not consistent between male and female as to which category it was in." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Survival Rates by Age and Gender:\n", + "Infant Males (14):\t0.642857142857\n", + "Infant Females (10):\t0.6\n", + "Child Males (44):\t0.318181818182\n", + "Child Females (45):\t0.711111111111\n", + "Adult Males (395):\t0.177215189873\n", + "Adult Females (206):\t0.771844660194\n" + ] + } + ], + "source": [ + "infant_males = df[(df.Age <= 2) & (df.Sex == 'male')]\n", + "infant_females = df[(df.Age <= 2) & (df.Sex == 'female')]\n", + "child_males = df[(df.Age > 2) & (df.Age < 18) & (df.Sex == 'male')]\n", + "child_females = df[(df.Age > 2) & (df.Age < 18) & (df.Sex == 'female')]\n", + "adult_males = df[(df.Age >= 18) & (df.Sex == 'male')]\n", + "adult_females = df[(df.Age >= 18) & (df.Sex == 'female')]\n", + "\n", + "print 'Survival Rates by Age and Gender:'\n", + "print 'Infant Males ({}):\\t'.format(len(infant_males)), getRate(infant_males)\n", + "print 'Infant Females ({}):\\t'.format(len(infant_females)), getRate(infant_females)\n", + "print 'Child Males ({}):\\t'.format(len(child_males)), getRate(child_males)\n", + "print 'Child Females ({}):\\t'.format(len(child_females)), getRate(child_females)\n", + "print 'Adult Males ({}):\\t'.format(len(adult_males)), getRate(adult_males)\n", + "print 'Adult Females ({}):\\t'.format(len(adult_females)), getRate(adult_females)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a couple of surprises here. Adult females had a greater survival rate than child females, but not by a huge amount, and both were quite high. Child males had a higher survival rate than adult males but a lower survival rate than child females, but infant males had a higher survival rate than infant females. Recalling the 'women and children first' saying, it makes sense that infants had around the same survival rate regardless of gender. However, it seems like the definition of 'children' might have had a lower upper bound than the age of 18 (which was how I was classifying children)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing a relationship with a continuous feature" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plotSurvivalByAge(df_group, bin_step=1, new_fig=True):\n", + " \"\"\"\n", + " Given a group of passengers and bin size, plot the survival rate as a function of age.\n", + " \"\"\"\n", + " bins = np.arange(0, 80, bin_step)\n", + " indices = np.digitize(df_group.Age, bins)\n", + " groups = df_group.groupby(indices)\n", + "\n", + " ages = [group.Age.mean() for i, group in groups]\n", + " rates = [getRate(group) for i, group in groups]\n", + "\n", + " if new_fig:\n", + " plt.figure()\n", + " plt.plot(ages, rates)\n", + " plt.xlabel('Age (years)')\n", + " plt.ylabel('Survival Rate')\n", + " plt.axis([0, 80, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Only use passengers with known or estimated age\n", + "# Since estimates are coded as xx.5, it will affect things slightly, \n", + "# but we should still get the general idea due to binning\n", + "df = df[df.Age.notnull()]\n", + "\n", + "plotSurvivalByAge(df, 10)\n", + "plt.title('Survival Rate vs. Age')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These results are not surprising; survival rate decreases as age increases. I am interested in seeing the difference between genders and zooming in more on the younger end of the spectrum with less aggressive binning." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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vMcvGMODrAXSs3JFp66cx7455YT1c2UQma0ryQbzGM/7n8Tz+zeNcUfQKnm32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\nImjWDBo3hvz5/Y0vuWR0psS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "males = df[df.Sex == 'male']\n", + "females = df[df.Sex == 'female']\n", + "\n", + "plotSurvivalByAge(males, 10)\n", + "plotSurvivalByAge(females, 10, False)\n", + "plt.legend(['Male', 'Female'], loc=0)\n", + "plt.title('Survival Rate vs. Age by Gender')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given what we've seen before, this is not surprising either. Young children has similar survival rates regardless of gender, men in their teens and older has low survival rates, and women in general had higher survival rates." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Jk1atnOEzbrjBuZL7iSfCqztrTOkYhncaTuKkRJbds4xKxSt5HZLJg5S2hSV9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\nbVs4cCDjpHHqVMZJo3x5Sxr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "child_males = males[males.Age < 18]\n", + "child_females = females[females.Age < 18]\n", + "\n", + "plotSurvivalByAge(child_males, 3)\n", + "plotSurvivalByAge(child_females, 3, False)\n", + "plt.legend(['Male', 'Female'], loc=0)\n", + "plt.axis([0, 18, 0, 1])\n", + "plt.title('Survival Rate vs. Age by Gender for Children')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is interesting that the survival rate for male children has a downward trend as age increases, but the survival rate for female children varies more. My initial thought is that the fourth bin of female children (with a survival rate of 0.0) might have zero or only one person in it. Let's check." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 11, 5, 6, 7, 16]\n", + "[(0.75, 2.0), (3.0, 5.0), (6.0, 8.0), (9.0, 11.0), (13.0, 14.5), (15.0, 17.0)]\n" + ] + } + ], + "source": [ + "bins = np.arange(0, 80, 3)\n", + "indices = np.digitize(child_females.Age, bins)\n", + "groups = child_females.groupby(indices)\n", + "print [len(group) for i, group in groups]\n", + "print [(group.Age.min(), group.Age.max()) for i, group in groups]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It turns out that the fourth bin had six passengers in it, so the 0.0 survival rate means that all six of the female children between 9 and 11 years of age did not survive the incident. From a statistical significance standpoint, six is not a very large number, but it is still surprising to me that there was a group of female children with a survival rate of 0.0 given the trends previously observed." + ] + } + ], + "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": "2.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_1.ipynb b/model_iteration_1.ipynb new file mode 100644 index 0000000..818dd43 --- /dev/null +++ b/model_iteration_1.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import cross_validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and clean data" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cleanData(data, median_age):\n", + " \"\"\"\n", + " Take in the raw data and median age from the training data\n", + " and return a cleaned version for use with our model\n", + " \"\"\"\n", + " \n", + " # Replace missing ages with the median age (from the training data!)\n", + " data.Age = data.Age.fillna(median_age)\n", + "\n", + " # Encode male as 0 and female as 1\n", + " data.loc[data.Sex == 'male', 'Sex'] = 0\n", + " data.loc[data.Sex == 'female', 'Sex'] = 1\n", + "\n", + " # Replace missing port of embarkation with Southampton\n", + " # Emcode Southampton as 0, Cherbourg as 1, and Queenstown as 2\n", + " data.Embarked = data.Embarked.fillna('S')\n", + " data.loc[data.Embarked == 'S', 'Embarked'] = 0\n", + " data.loc[data.Embarked == 'C', 'Embarked'] = 1\n", + " data.loc[data.Embarked == 'Q', 'Embarked'] = 2\n", + " \n", + " # Replace missing fares with the median fare\n", + " data.Fare = data.Fare.fillna(data.Fare.median())\n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "titanic = pd.read_csv('train.csv')\n", + "titanic_test = pd.read_csv('test.csv')\n", + "\n", + "titanic = cleanData(titanic, titanic.Age.median())\n", + "titanic_test = cleanData(titanic_test, titanic.Age.median())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use linear regression to create a model" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78.339% accuracy\n" + ] + } + ], + "source": [ + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "\n", + "# Initialize our algorithm class\n", + "alg = LinearRegression()\n", + "# Generate cross validation folds for the titanic dataset. It return the row indices corresponding to train and test.\n", + "# We set random_state to ensure we get the same splits every time we run this.\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train, test in kf:\n", + " # The predictors we're using the train the algorithm. Note how we only take the rows in the train folds.\n", + " train_predictors = (titanic[predictors].iloc[train,:])\n", + " # The target we're using to train the algorithm.\n", + " train_target = titanic.Survived.iloc[train]\n", + " # Training the algorithm using the predictors and target.\n", + " alg.fit(train_predictors, train_target)\n", + " # We can now make predictions on the test fold\n", + " test_predictions = alg.predict(titanic[predictors].iloc[test,:])\n", + " predictions.append(test_predictions)\n", + "\n", + "# The predictions are in three separate numpy arrays. Concatenate them into one. \n", + "# We concatenate them on axis 0, as they only have one axis.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "# Map predictions to outcomes (only possible outcomes are 1 and 0)\n", + "predictions[predictions > .5] = 1\n", + "predictions[predictions <=.5] = 0\n", + "\n", + "# Calculate accuracy\n", + "accuracy = sum(titanic.Survived == predictions) / len(titanic)\n", + "print '{:.3f}% accuracy'.format(accuracy*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use logistic regression to create a model" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78.788% accuracy\n" + ] + } + ], + "source": [ + "# Set predictors\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "# Initialize our algorithm\n", + "alg = LogisticRegression(random_state=1)\n", + "# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Apply model to test data" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def xValidateLogModel(train, predictors):\n", + " \"\"\"\n", + " Take in the training data and predictors to use\n", + " and return the score of the cross validation\n", + " \"\"\"\n", + " # Initialize the algorithm class\n", + " alg = LogisticRegression(random_state=1)\n", + " \n", + " # Cross validate using training data\n", + " scores = cross_validation.cross_val_score(alg, train[predictors], train.Survived, cv=3)\n", + " \n", + " # Return the mean of the 3 scores\n", + " return scores.mean()\n", + "\n", + "def testLogModel(train, test, predictors):\n", + " \"\"\"\n", + " Take in the training data, testing data, and predictors to use\n", + " and return the submission dataframe for a logistic regression model\n", + " \"\"\"\n", + " # Initialize the algorithm class\n", + " alg = LogisticRegression(random_state=1)\n", + " \n", + " # Train the algorithm using all the training data\n", + " alg.fit(train[predictors], train.Survived)\n", + "\n", + " # Make predictions using the test set.\n", + " predictions = alg.predict(test[predictors])\n", + "\n", + " # Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + " return pd.DataFrame({\n", + " 'PassengerId': test.PassengerId,\n", + " 'Survived': predictions\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Set predictors\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "\n", + "# Test model\n", + "submission = testLogModel(titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_1.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "75.120% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Second model iteration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A change I would like to make to the model is to ignore the port of embarkation. It doesn't seem like this factor should influence a passenger's survival. When exploring the data, I did find that there was a difference in survival rates between the ports, but I hypothesize that this has to do with the percentages of upper class, female, and child passengers from each place rather than anything about the port itself. So, it seems odd to me to include the port of embarkation in the model." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79.012% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\"]\n", + "score = xValidateLogModel(titanic, predictors)\n", + "print '{:.3f}% accuracy'.format(score*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cross validation shows a slight improvement, so let's try it on the test data!" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\"]\n", + "submission = testLogModel(titanic, titanic_test, predictors)\n", + "submission.to_csv('submission_2.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "74.163% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though the cross validation with the training data showed an improvement, the model performed worse with the test data. I'm a bit confused as to why this may be. Does having the port of embarkation included give more weight to the underlying factors that differentiate the ports (proportions of class, sex, age, etc.)? If so, why doesn't the model give these factors an appropriate weighting when the port information is removed? If not, why _is_ the port of embarkation information being useful when my intuition is saying that it should not be?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Third model iteration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Something else seen when exploring the data is that young children tended to survive regardless of gender. By adding a flag for young children, I hope to bring this out more." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80.471% accuracy\n" + ] + } + ], + "source": [ + "titanic.loc[titanic.Age <= 8, 'IsChild'] = 1\n", + "titanic.loc[titanic.Age > 8, 'IsChild'] = 0\n", + "titanic_test.loc[titanic_test.Age <= 8, 'IsChild'] = 1\n", + "titanic_test.loc[titanic_test.Age > 8, 'IsChild'] = 0\n", + "\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"IsChild\"]\n", + "score = xValidateLogModel(titanic, predictors)\n", + "print '{:.3f}% accuracy'.format(score*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is showing a greater improvement than the last iteration, but let's see if it can improve with the test data." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"IsChild\"]\n", + "submission = testLogModel(titanic, titanic_test, predictors)\n", + "submission.to_csv('submission_3.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "76.555% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It did improve! The new model was able to guess correctly for 6 more of the test passengers than in the initial iteration. Honestly, I'm not entirely sure why this improved the model. Perhaps a logistic regression cannot deal with a step, like the one in the survival rate of males between children and adults, and adding a feature that reflects the step helped?" + ] + } + ], + "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": "2.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_2.ipynb b/model_iteration_2.ipynb new file mode 100644 index 0000000..f4fa66f --- /dev/null +++ b/model_iteration_2.ipynb @@ -0,0 +1,953 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the DataQuest Kaggle module \"Improving your submission\" as a resource in my second model iteration. The first thing it did was introduce the random forest model, so I will start by creating one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn import cross_validation\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.feature_selection import SelectKBest, f_classif\n", + "import re\n", + "import operator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and clean data" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cleanData(data, median_age):\n", + " \"\"\"\n", + " Take in the raw data and median age from the training data\n", + " and return a cleaned version for use with our model\n", + " \"\"\"\n", + " # Replace missing ages with the median age (from the training data!)\n", + " data.Age = data.Age.fillna(median_age)\n", + "\n", + " # Encode male as 0 and female as 1\n", + " data.loc[data.Sex == 'male', 'Sex'] = 0\n", + " data.loc[data.Sex == 'female', 'Sex'] = 1\n", + "\n", + " # Replace missing port of embarkation with Southampton\n", + " # Emcode Southampton as 0, Cherbourg as 1, and Queenstown as 2\n", + " data.Embarked = data.Embarked.fillna('S')\n", + " data.loc[data.Embarked == 'S', 'Embarked'] = 0\n", + " data.loc[data.Embarked == 'C', 'Embarked'] = 1\n", + " data.loc[data.Embarked == 'Q', 'Embarked'] = 2\n", + " \n", + " # Replace missing fares with the median fare\n", + " data.Fare = data.Fare.fillna(data.Fare.median())\n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic = pd.read_csv('train.csv')\n", + "titanic_test = pd.read_csv('test.csv')\n", + "\n", + "titanic = cleanData(titanic, titanic.Age.median())\n", + "titanic_test = cleanData(titanic_test, titanic.Age.median())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a random forest model" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80.022% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Already it's looking like a random forest model is doing quite a bit better than the logistic regression from iteration 1!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tweak model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81.930% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Increasing the number of trees used, number of samples required to split, and number of samples required to create a leaf improved the cross validation accuracy by nearly 2% and will help avoid overfitting to the training data. Let's see how this model does with the test data." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def testModel(alg, train, test, predictors):\n", + " \"\"\"\n", + " Take in the training data, testing data, and predictors to use\n", + " and return the submission dataframe for a logistic regression model\n", + " \"\"\"\n", + " # Train the algorithm using all the training data\n", + " alg.fit(train[predictors], train.Survived)\n", + "\n", + " # Make predictions using the test set.\n", + " predictions = alg.predict(test[predictors])\n", + "\n", + " # Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + " return pd.DataFrame({\n", + " 'PassengerId': test.PassengerId,\n", + " 'Survived': predictions\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_4.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "75.120 accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though the cross validation score was higher than any from the logistic regression model, the random forest model performed the same on the test data as our very first submission. It seems to me that random forests may be more susceptible to overfitting than the logistic regression, so I will have to be aware that a better cross validation score may not mean that a model will be more accurate with the test data. I'll deviate from the DataQuest module and try increasing `min_samples_split` and `min_samples_leaf` a bit more to see if that helps." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81.706% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As I expected, the cross validation score was a bit lower. This may indicate that the model has less overfitting and will perform better with the test data. Let's check." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_5.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "77.990% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model is the most accurate so far! I was correct in thinking that this model would be less overfit to the training data and thus do better with the test data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next thing the DataQuest module did was create new features. I'll follow along, create some of my own, then evaluate how much of an impact these features have." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create new features" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def getTitle(name):\n", + " \"\"\"\n", + " Take in a name and return the title encoded as an integer, if there is one\n", + " \"\"\"\n", + " # Use a regular expression to search for a title.\n", + " # Titles always consist of capital and lowercase letters, and end with a period.\n", + " title_search = re.search(' ([A-Za-z]+)\\.', name)\n", + " # If the title exists, extract and encode it.\n", + " if title_search:\n", + " title = title_search.group(1)\n", + "\n", + " # Map each title to an integer.\n", + " # Some titles are very rare, and are compressed into the same codes as other titles.\n", + " title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 6,\n", + " \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Lady\": 10,\n", + " \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\n", + " try:\n", + " return title_mapping[title]\n", + " except KeyError:\n", + " pass\n", + " return 0\n", + "\n", + "def getFamilyId(row, family_id_mapping):\n", + " \"\"\"\n", + " Take in a row and the family id map and return the family id and updated map\n", + " Must be run after FamilySize column is created\n", + " \"\"\"\n", + " # Find the last name by splitting on a comma\n", + " last_name = row.Name.split(\",\")[0]\n", + " # Create the family id\n", + " family_id = '{}{}'.format(last_name, row.FamilySize)\n", + " # Look up the id in the mapping\n", + " if family_id not in family_id_mapping:\n", + " if len(family_id_mapping) == 0:\n", + " current_id = 1\n", + " else:\n", + " # Get the maximum id from the mapping and add one to it if we don't have an id\n", + " current_id = (max(family_id_mapping.items(), key=operator.itemgetter(1))[1] + 1)\n", + " family_id_mapping[family_id] = current_id\n", + " return family_id_mapping[family_id], family_id_mapping\n", + "\n", + "def createDQFeatures(data, family_id_mapping):\n", + " \"\"\"\n", + " Add the features from the DataQuest module\n", + " \"\"\"\n", + " # Generating a familysize column\n", + " data[\"FamilySize\"] = data.SibSp + data.Parch\n", + "\n", + " # The .apply method generates a new series\n", + " data[\"NameLength\"] = data.Name.apply(lambda x: len(x))\n", + "\n", + " # Add a title column\n", + " data[\"Title\"] = data.Name.apply(getTitle)\n", + "\n", + " # Add a family id column\n", + " family_ids = pd.Series(np.zeros((len(data),)))\n", + " # Unlike in DataQuest, I don't want to use global variables\n", + " # Unfortunately, this means I have to iterate over the rows manually\n", + " for i, row in data.iterrows():\n", + " family_id, family_id_mapping = getFamilyId(row, family_id_mapping)\n", + " family_ids[i] = family_id\n", + " # There are a lot of family ids, so we'll compress all of the families under 3 members into one code.\n", + " family_ids[titanic.FamilySize < 3] = -1\n", + " data[\"FamilyId\"] = family_ids\n", + "\n", + " return data, family_id_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# We want to use the same family ID map in the training and test data, so we need to keep\n", + "# track of it (and I don't want to use global variables like the DataQuest module does)\n", + "family_id_mapping = {}\n", + "titanic, family_id_mapping = createDQFeatures(titanic, family_id_mapping)\n", + "titanic_test, family_id_mapping = createDQFeatures(titanic_test, family_id_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def getTitle2(name):\n", + " \"\"\"\n", + " Take in a name and return the title encoded as an integer, if there is one\n", + " \"\"\"\n", + " # Use a regular expression to search for a title.\n", + " # Titles always consist of capital and lowercase letters, and end with a period.\n", + " title_search = re.search(' ([A-Za-z]+)\\.', name)\n", + " # If the title exists, extract and encode it.\n", + " if title_search:\n", + " title = title_search.group(1)\n", + "\n", + " # Map each title to an integer. (Revised groupings by me)\n", + " # 1: Mr (adult male)\n", + " # 2: Miss, Mlle, Ms (young female)\n", + " # 3: Mrs, Mme (adult female)\n", + " # 4: Master (young male)\n", + " # 5: Dr, Rev (special adult)\n", + " # 6: Major, Col (military)\n", + " # 7: Don, Jonkheer, Sir, Capt (very special male)\n", + " # 8: Lady, Countess (very special female)\n", + " title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 5,\n", + " \"Major\": 6, \"Col\": 6, \"Mlle\": 2, \"Mme\": 3, \"Don\": 7, \"Lady\": 8,\n", + " \"Countess\": 8, \"Jonkheer\": 7, \"Sir\": 7, \"Capt\": 7, \"Ms\": 2}\n", + " try:\n", + " return title_mapping[title]\n", + " except KeyError:\n", + " pass\n", + " return 0\n", + "\n", + "def createMyFeatures(data):\n", + " \"\"\"\n", + " Add my features\n", + " \"\"\"\n", + " # Add a flag for children under the age of 9\n", + " data[\"IsChild\"] = data.Age.apply(lambda x: 1 if x < 9 else 0)\n", + " \n", + " # Add another family size column that includes the passenger (like we talked about in class)\n", + " data[\"FamilySize2\"] = data.SibSp + data.Parch + 1\n", + " \n", + " # Add another title column that groups the titles differently\n", + " data[\"Title2\"] = data.Name.apply(getTitle2)\n", + "\n", + " return data\n", + "\n", + "titanic = createMyFeatures(titanic)\n", + "titanic_test = createMyFeatures(titanic_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Select best features to use" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(68.851994252857665, 'Sex')\n", + "(32.002333704918456, 'Title2')\n", + "(26.983386072106185, 'Title')\n", + "(24.595671420777524, 'Pclass')\n", + "(23.693190161546514, 'NameLength')\n", + "(14.213235141762933, 'Fare')\n", + "(5.0206988771330954, 'IsChild')\n", + "(2.8513009904508668, 'Embarked')\n", + "(1.8716004089590674, 'FamilyId')\n", + "(1.8297604290610845, 'Parch')\n", + "(1.2776895459668496, 'Age')\n", + "(0.53425450244248629, 'SibSp')\n", + "(0.20768458341872537, 'FamilySize2')\n", + "(0.20768458341872537, 'FamilySize')\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\",\n", + " \"FamilySize\", \"NameLength\", \"Title\", \"FamilyId\", \n", + " \"IsChild\", \"FamilySize2\", \"Title2\"]\n", + "\n", + "# Perform feature selection\n", + "selector = SelectKBest(f_classif, k='all')\n", + "selector.fit(titanic[predictors], titanic[\"Survived\"])\n", + "\n", + "# Get the raw p-values for each feature, and transform from p-values into scores\n", + "scores = -np.log10(selector.pvalues_)\n", + "\n", + "for x in sorted(zip(scores, predictors))[::-1]:\n", + " print x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the DataQuest module, they didn't include name length in this part, so I was surprised to see how high it ranked among the predictors. Maybe the `f_classif` function rated it highly, but it doesn't do much for the random forest. After seeing some of the data from the name column, it would seem to me that name length would not be that useful for indicating wealth which is what the DataQuest module intended when creating that column. I was also surprised that title was the only other new feature in the top five. It looks like my encodings of the titles were slightly better than the ones from the DataQuest module, but including the passenger in the family size had no effect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, I want to see how adding predictors one-by-one impacts cross validation score." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: 78.676% accuracy\n", + "2: 79.349% accuracy\n", + "3: 77.778% accuracy\n", + "4: 81.369% accuracy\n", + "5: 80.808% accuracy\n", + "6: 82.267% accuracy\n", + "7: 82.492% accuracy\n", + "8: 82.492% accuracy\n" + ] + } + ], + "source": [ + "# Predictors in order of importance, leaving out NameLength and using Title2 instead of Title\n", + "predictors = [\"Sex\", \"Title2\", \"Pclass\", \"Fare\", \"IsChild\", \"Embarked\", \"FamilyId\", \"FamilySize\"]\n", + "\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "for i in range(len(predictors)):\n", + " scores = cross_validation.cross_val_score(alg, titanic[predictors[:i+1]], titanic.Survived, cv=3)\n", + " print '{}: {:.3f}% accuracy'.format(i+1, scores.mean()*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a couple of times when adding more predictors reduces accuracy, which is surprising to me. I'll try a few different sets of predictors to see how they do with the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Sex\", \"Title2\", \"Pclass\", \"Fare\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_6.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "77.033% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using only the top 4 predictors performed worse than the previous submission that used none of the added features. This isn't too surprising since it had a slighly lower cross validation score." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Sex\", \"Title2\", \"Pclass\", \"Fare\", \"IsChild\", \"Embarked\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_7.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "75.598% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding the next 2 predictors decreased the accuracy by over 1%. Judging by the cross validation scores, this model should have done better. This may be an indication that the model is overfitting." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Sex\", \"Title2\", \"Pclass\", \"Fare\", \"IsChild\", \"Embarked\", \"FamilyId\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_8.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "76.077% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding the family ID improved performance slightly, but it is still worse than just using the top 4 predictors. It seems like using fewer predictors helps prevent overfitting and allows the model to perform better with the test data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far, none of the models that use the new features have improved with the test data. So, I want to see how a model does that _only_ uses the new features (except for name length)." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.267% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Title2\", \"IsChild\", \"FamilyId\", \"FamilySize\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "predictors = [\"Title2\", \"IsChild\", \"FamilyId\", \"FamilySize\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_9.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "78.469% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This improved our best performance! It is interesting that the top 4 predictors according to `SelectKBest` and `f_classif` were not as good as the 4 added predictors. It seems that using a smaller number of predictors is good for preventing overfitting and `SelectKBest` is not necessarily good at selecting the best predictors. I'm going to choose a set of predictors based on my intuition while trying to keep the number low." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.941% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Sex\", \"Pclass\", \"Age\", \"Title2\", \"FamilyId\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Sex\", \"Pclass\", \"Age\", \"Title2\", \"FamilyId\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_10.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "74.163% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one did quite a bit worse, even though the cross validation score looked good. I'm guessing that overfitting in the culprit again, prossibly due to using the family ID. I wonder if replacing it with family size will help." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.492% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Sex\", \"Pclass\", \"Age\", \"Title2\", \"FamilySize\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Sex\", \"Pclass\", \"Age\", \"Title2\", \"FamilySize\"]\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_11.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "74.641% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one also did worse than the model using only the added features. I think it's time to try something other than just choosing different predictors." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Gradient boosting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Gradient boosting involves training the decision trees one at a time and using the errors to help build the next tree. Since this can easily lead to overfitting, the DataQuest module suggests limiting the number of trees to 25 and the tree depth to 3. First, I'll use the predictors that the module uses (with my modified title groupings)." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.155% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\", \"FamilySize\", \"Title2\", \"FamilyId\"]\n", + "alg = GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\", \"FamilySize\", \"Title2\", \"FamilyId\"]\n", + "alg = GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_12.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "80.383% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The gradient boosting boosted our score to the highest yet! This was also the smallest difference between cross validation score and test score so far, perhaps indicating that this model did not suffer from overfitting as much as the previous ones. This is interesting to me since the DataQuest module used a lot of columns as predictors, so now I'm going to try using only the predictors from the best random forest—the created features." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "83.165% accuracy\n" + ] + } + ], + "source": [ + "predictors = [\"Title2\", \"IsChild\", \"FamilyId\", \"FamilySize\"]\n", + "alg = GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic.Survived, cv=3)\n", + "print '{:.3f}% accuracy'.format(scores.mean()*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictors = [\"Title2\", \"IsChild\", \"FamilyId\", \"FamilySize\"]\n", + "alg = GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3)\n", + "submission = testModel(alg, titanic, titanic_test, predictors)\n", + "\n", + "# Write submission to a CSV file\n", + "submission.to_csv('submission_13.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "77.512% accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one didn't do as well, and had a large difference between cross validation score and test score. I guess with this model, using a large number of predictors can work out pretty well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Future explorations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next thing that the DataQuest module did was use both gradient boosting and a logistic regression as an ensemble. However, their accuracy with the test data was slightly (1 or 2 predictions) lower than what I got with the gradient boosting alone, so I'm not sure if this would be worth pursuing. With both the random forest and gradient boosting, I would be interested in looking into a better way of choosing which features to use as predictors. `SelectKBest` didn't seem to do a great job, and my intuitions often did worse than I expected. With the way Kaggle is set up, it's impossible to do an exhaustive search using the test data, and as we've seen in this iteration, higher cross validation scores do not necessarily mean better test accuracy. Is there a good way to search across features to find the best ones to use as predictors?" + ] + } + ], + "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": "2.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/submission_1.csv b/submission_1.csv new file mode 100644 index 0000000..485997c --- /dev/null +++ b/submission_1.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0 +893,0 +894,0 +895,0 +896,1 +897,0 +898,1 +899,0 +900,1 +901,0 +902,0 +903,0 +904,1 +905,0 +906,1 +907,1 +908,0 +909,0 +910,1 +911,1 +912,0 +913,0 +914,1 +915,1 +916,1 +917,0 +918,1 +919,0 +920,0 +921,0 +922,0 +923,0 +924,0 +925,1 +926,0 +927,0 +928,1 +929,1 +930,0 +931,0 +932,0 +933,0 +934,0 +935,1 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