diff --git a/data_exploration.ipynb b/data_exploration.ipynb new file mode 100644 index 0000000..353a66b --- /dev/null +++ b/data_exploration.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Imports\n", + "\n", + "# pandas\n", + "import pandas as pd\n", + "from pandas import Series,DataFrame\n", + "\n", + "# numpy, matplotlib, seaborn\n", + "import math\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from sklearn.linear_model import LogisticRegression\n", + "sns.set_style('whitegrid')\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get titanic & test csv files as a DataFrame\n", + "titanic_df = pd.read_csv(\"train.csv\", dtype={\"Age\": np.float64}, )\n", + "test_df = pd.read_csv(\"test.csv\", dtype={\"Age\": np.float64}, )\n", + "\n", + "# preview the data\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n", + " u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Male Survival Percent: 0.188908145581\n", + "Female Survival Percent: 0.742038216561\n" + ] + } + ], + "source": [ + "male = titanic_df[titanic_df.Sex == 'male']\n", + "female = titanic_df[titanic_df.Sex == 'female']\n", + "print \"Male Survival Percent: \" + str(male.Survived.mean())\n", + "print \"Female Survival Percent: \" + str(female.Survived.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Men were more likely to die. Shocking /s" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def Cov(xs, ys, meanx=None, meany=None):\n", + " xs = np.asarray(xs)\n", + " ys = np.asarray(ys)\n", + " if meanx is None:\n", + " meanx = np.mean(xs)\n", + " if meany is None:\n", + " meany = np.mean(ys)\n", + " \n", + " cov = np.dot(xs-meanx, ys-meany) / len(xs)\n", + " return cov\n", + "\n", + "def Corr(xs, ys):\n", + " xs = np.asarray(xs)\n", + " ys = np.asarray(ys)\n", + " meanx = np.mean(xs)\n", + " varx = np.var(xs)\n", + " meany = np.mean(ys)\n", + " vary = np.var(ys)\n", + " corr = Cov(xs, ys, meanx, meany) / math.sqrt(varx * vary)\n", + " return corr\n", + "\n", + "def SpearmanCorr(xs, ys):\n", + " xranks = pd.Series(xs).rank()\n", + " yranks = pd.Series(ys).rank()\n", + " return Corr(xranks, yranks)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here I recode a few of the categories (Sex and Embarked) into Binary data. The Embarked column has been split into 3 binary columns ('EmbarkedS','EmbarkedC','EmbarkedQ')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "columns = ['Pclass', 'SexNum', 'Age', 'SibSp', 'Parch', 'Fare', 'EmbarkedS','EmbarkedC','EmbarkedQ']\n", + "oldNewMapSex = {'male': 1, 'female': 0}\n", + "oldNewMapS = {'S': 1, 'C': 0, 'Q': 0}\n", + "oldNewMapC = {'S': 0, 'C': 1, 'Q': 0}\n", + "oldNewMapQ = {'S': 0, 'C': 0, 'Q': 1}\n", + "titanic_df['SexNum'] = titanic_df['Sex'].map(oldNewMapSex)\n", + "titanic_df['EmbarkedS'] = titanic_df['Embarked'].map(oldNewMapS)\n", + "titanic_df['EmbarkedC'] = titanic_df['Embarked'].map(oldNewMapC)\n", + "titanic_df['EmbarkedQ'] = titanic_df['Embarked'].map(oldNewMapQ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here I compute and display the Pearson and Spearman Correlations for a variety or variables and survived. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass\n", + "Pearson Correlations: -0.338481035961\n", + "Spearman Correlations: -0.33966793665\n", + "SexNum\n", + "Pearson Correlations: -0.543351380658\n", + "Spearman Correlations: -0.543351380658\n", + "Age\n", + "Pearson Correlations: -0.0772210945722\n", + "Spearman Correlations: -0.0525653000447\n", + "SibSp\n", + "Pearson Correlations: -0.0353224988857\n", + "Spearman Correlations: 0.0888794846809\n", + "Parch\n", + "Pearson Correlations: 0.0816294070835\n", + "Spearman Correlations: 0.138265632865\n", + "Fare\n", + "Pearson Correlations: 0.257306522385\n", + "Spearman Correlations: 0.323736139445\n", + "EmbarkedS\n", + "Pearson Correlations: -0.151777048594\n", + "Spearman Correlations: -0.151777048594\n", + "EmbarkedC\n", + "Pearson Correlations: 0.169965966813\n", + "Spearman Correlations: 0.169965966813\n", + "EmbarkedQ\n", + "Pearson Correlations: 0.00453572872399\n", + "Spearman Correlations: 0.00453572872399\n" + ] + } + ], + "source": [ + "for column in columns:\n", + " titanic_dfNoNA = titanic_df.dropna(subset=['Survived', column])\n", + " print column\n", + " \n", + " print \"Pearson Correlations: \" + str(Corr(titanic_dfNoNA[column],titanic_dfNoNA.Survived))\n", + " print \"Spearman Correlations: \" + str(SpearmanCorr(titanic_dfNoNA[column],titanic_dfNoNA.Survived))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prep data for logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "titanic_dfReg = titanic_df.drop(['PassengerId','Name','Ticket','Embarked','Sex','Cabin'], axis=1)\n", + "titanic_dfReg = titanic_dfReg.dropna()\n", + "predData = titanic_dfReg.drop(\"Survived\",axis=1)\n", + "survived = titanic_dfReg[\"Survived\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run Logistic Regression and compute the score on the initial data" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7963483146067416" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = LogisticRegression()\n", + "logreg.fit(predData, survived)\n", + "logreg.score(predData, survived)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the coefficeints for each variable from the logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass: -0.917313200045\n", + "Age: -0.0326142424397\n", + "SibSp: -0.307305272134\n", + "Parch: -0.0512627096075\n", + "Fare: 0.00366830420125\n", + "SexNum: -2.38470231233\n", + "EmbarkedS: 1.05092552022\n", + "EmbarkedC: 1.41775488857\n", + "EmbarkedQ: 0.510784246869\n" + ] + } + ], + "source": [ + "coeffs = logreg.coef_[0]\n", + "name = titanic_dfReg.columns[1:]\n", + "for i in range(len(coeffs)):\n", + " print name[i] + \": \" + str(coeffs[i])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_1.ipynb b/model_iteration_1.ipynb new file mode 100644 index 0000000..4f2987b --- /dev/null +++ b/model_iteration_1.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 \n", + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 \n" + ] + } + ], + "source": [ + "import pandas\n", + "\n", + "# We can use the pandas library in python to read in the csv file.\n", + "# This creates a pandas dataframe and assigns it to the titanic variable.\n", + "titanic = pandas.read_csv(\"train.csv\")\n", + "\n", + "# Print the first 5 rows of the dataframe.\n", + "print titanic.head(5)\n", + "print titanic.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['male' 'female']\n" + ] + } + ], + "source": [ + "# Find all the unique genders -- the column appears to contain only male and female.\n", + "print(titanic[\"Sex\"].unique())\n", + "\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "\n", + "# Replace all the occurences of female with the number 1.\n", + "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['S' 'C' 'Q' nan]\n" + ] + } + ], + "source": [ + "# Find all the unique values for \"Embarked\".\n", + "print(titanic[\"Embarked\"].unique())\n", + "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "# oldNewMap = {'S': 0, 'C': 1, 'Q': 2}\n", + "# titanic['Embarked'] = titanic['Embarked'].map(oldNewMap)\n", + "\n", + "# print(titanic)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import the linear regression class\n", + "from sklearn.linear_model import LinearRegression\n", + "# Sklearn also has a helper that makes it easy to do cross validation\n", + "from sklearn.cross_validation import KFold\n", + "\n", + "# 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)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mingram/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:11: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n" + ] + } + ], + "source": [ + "import numpy as np\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", + "accuracy = sum(predictions[predictions == titanic[\"Survived\"]]) / len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.787878787879\n" + ] + } + ], + "source": [ + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "\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 scores.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "titanic_test = pandas.read_csv(\"test.csv\")\n", + "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n", + "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0 \n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Initialize the algorithm class\n", + "alg = LogisticRegression(random_state=1)\n", + "\n", + "# Train the algorithm using all the training data\n", + "alg.fit(titanic[predictors], titanic[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictions = alg.predict(titanic_test[predictors])\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pandas.DataFrame({\n", + " \"PassengerId\": titanic_test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "submission.to_csv(\"kaggle.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See if total family onboard matters (appears to slightly improve the predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.791245791246\n" + ] + } + ], + "source": [ + "import pandas\n", + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "\n", + "titanic = pandas.read_csv(\"train.csv\")\n", + "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "\n", + "# Replace all the occurences of female with the number 1.\n", + "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "titanic[\"Family\"] = titanic[\"SibSp\"] + titanic[\"Parch\"] +1\n", + "\n", + "\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"Family\"]\n", + "\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 scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Produce submission file for previous modification. This addition produced a semi-significant improvement (0.75120 -> 0.78469). The most notable thing about this test is that I initially did not add 1 (to account for the individual in question) to the Family. Naturally, this produced no difference from the original implementation. I think its very interesting that offsetttng a variable by 1 can produce such a noticable effect." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "136\n" + ] + } + ], + "source": [ + "titanic_test = pandas.read_csv(\"test.csv\")\n", + "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic_test[\"Age\"].median())\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "\n", + "# Replace all the occurences of female with the number 1.\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n", + "\n", + "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "titanic_test[\"Family\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\n", + "\n", + "\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"Family\"]\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(titanic[predictors], titanic[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictions = alg.predict(titanic_test[predictors])\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pandas.DataFrame({\n", + " \"PassengerId\": titanic_test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "submission.to_csv(\"kaggle.csv\", index=False)\n", + "print sum(predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interation 2. Investigate whether taking the log of the fares produces better results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.795735129068\n" + ] + } + ], + "source": [ + "import pandas\n", + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "\n", + "\n", + "titanic = pandas.read_csv(\"train.csv\")\n", + "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "\n", + "# Replace all the occurences of female with the number 1.\n", + "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "titanic[\"Family\"] = titanic[\"SibSp\"] + titanic[\"Parch\"] +1\n", + "\n", + "titanic[\"logFare\"] = np.log(titanic[\"Fare\"] + .01)\n", + "\n", + "\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"logFare\", \"Embarked\"]\n", + "\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 scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate submission file for interation 2. Produced a notable decrease in accuracy on the test data (0.78469 -> 0.74641) despite showing a slight improvement in predicting the training data (0.79124-> 0.79685). I'm not exactly sure what accounted for this difference..." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "titanic_test = pandas.read_csv(\"test.csv\")\n", + "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic_test[\"Age\"].median())\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "\n", + "# Replace all the occurences of female with the number 1.\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n", + "\n", + "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n", + "\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "titanic_test[\"Family\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"] +1\n", + "\n", + "titanic_test[\"logFare\"] = np.log(titanic_test[\"Fare\"] + .01)\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"logFare\", \"Embarked\"]\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(titanic[predictors], titanic[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictions = alg.predict(titanic_test[predictors])\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pandas.DataFrame({\n", + " \"PassengerId\": titanic_test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "submission.to_csv(\"kaggle.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_2.ipynb b/model_iteration_2.ipynb new file mode 100644 index 0000000..660fe98 --- /dev/null +++ b/model_iteration_2.ipynb @@ -0,0 +1,804 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating deaths on the titanic\n", + "This notebook cleans the data from the titanic kaggle competition and then uses 3 alorithms to try and predict who survived (Logistic Regression, Random Forest, Gradient Boosting). Much of the code was copied from or inspired by these 2 sources: https://www.dataquest.io/mission/75/improving-your-submission and https://github.com/elenacuoco/kaggle-competitions/blob/master/Titanic-For_Blog.ipynb. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The imports and setup" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import string\n", + "import operator\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "le = preprocessing.LabelEncoder()\n", + "enc=preprocessing.OneHotEncoder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rawdf=pd.read_csv(\"train.csv\")\n", + "rawdf_test=pd.read_csv(\"test.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaning the Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cleaning functions (source: https://github.com/elenacuoco/kaggle-competitions/blob/master/Titanic-For_Blog.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "###utility to clean and munge data\n", + "def substrings_in_string(big_string, substrings):\n", + " for substring in substrings:\n", + " if string.find(big_string, substring) != -1:\n", + " return substring\n", + " print big_string\n", + " return np.nan\n", + "\n", + "# A dictionary mapping family name to id\n", + "family_id_mapping = {}\n", + "\n", + "def clean_and_munge_data(df):\n", + " #setting silly values to nan\n", + " df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\n", + " \n", + " #Creating new family_size column\n", + " df['Family_Size']=df['SibSp']+df['Parch']\n", + " df['Family']=df['SibSp']*df['Parch']\n", + " \n", + " #creating a title column from name\n", + " title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n", + " 'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n", + " 'Don', 'Jonkheer']\n", + " df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n", + "\n", + " #replacing all titles with mr, mrs, miss, master\n", + " def replace_titles(x):\n", + " title=x['Title']\n", + " if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n", + " return 'Mr'\n", + " elif title in ['Master']:\n", + " return 'Master'\n", + " elif title in ['Countess', 'Mme','Mrs']:\n", + " return 'Mrs'\n", + " elif title in ['Mlle', 'Ms','Miss']:\n", + " return 'Miss'\n", + " elif title =='Dr':\n", + " if x['Sex']=='Male':\n", + " return 'Mr'\n", + " else:\n", + " return 'Mrs'\n", + " elif title =='':\n", + " if x['Sex']=='Male':\n", + " return 'Master'\n", + " else:\n", + " return 'Miss'\n", + " else:\n", + " return title\n", + " \n", + " ##Family Grouping code taken from: https://www.dataquest.io/mission/75/improving-your-submission \n", + "\n", + " # A function to get the id given a row\n", + " def get_family_id(row):\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 = \"{0}{1}\".format(last_name, row[\"Family_Size\"])\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]\n", + " \n", + " # Get the family ids with the apply method\n", + " family_ids = df.apply(get_family_id, axis=1)\n", + "\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[df[\"Family_Size\"] < 3] = -1\n", + "\n", + " # Print the count of each unique id.\n", + " print(pd.value_counts(family_ids))\n", + "\n", + " df[\"FamilyId\"] = family_ids\n", + "\n", + " df['Title']=df.apply(replace_titles, axis=1)\n", + "\n", + "\n", + "\n", + "\n", + " #imputing nan values\n", + " df.loc[ (df.Fare.isnull())&(df.Pclass==1),'Fare'] =np.median(df[df['Pclass'] == 1]['Fare'].dropna())\n", + " df.loc[ (df.Fare.isnull())&(df.Pclass==2),'Fare'] =np.median( df[df['Pclass'] == 2]['Fare'].dropna())\n", + " df.loc[ (df.Fare.isnull())&(df.Pclass==3),'Fare'] = np.median(df[df['Pclass'] == 3]['Fare'].dropna())\n", + "\n", + " df['AgeFill']=df['Age']\n", + " mean_ages = np.zeros(4)\n", + " mean_ages[0]=np.average(df[df['Title'] == 'Miss']['Age'].dropna())\n", + " mean_ages[1]=np.average(df[df['Title'] == 'Mrs']['Age'].dropna())\n", + " mean_ages[2]=np.average(df[df['Title'] == 'Mr']['Age'].dropna())\n", + " mean_ages[3]=np.average(df[df['Title'] == 'Master']['Age'].dropna())\n", + " df.loc[ (df.Age.isnull()) & (df.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n", + " df.loc[ (df.Age.isnull()) & (df.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n", + " df.loc[ (df.Age.isnull()) & (df.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n", + " df.loc[ (df.Age.isnull()) & (df.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n", + "\n", + " df['AgeCat']=df['AgeFill']\n", + " df.loc[ (df.AgeFill<=10) ,'AgeCat'] = 'child'\n", + " df.loc[ (df.AgeFill>60),'AgeCat'] = 'aged'\n", + " df.loc[ (df.AgeFill>10) & (df.AgeFill <=30) ,'AgeCat'] = 'adult'\n", + " df.loc[ (df.AgeFill>30) & (df.AgeFill <=60) ,'AgeCat'] = 'senior'\n", + "\n", + " df.Embarked = df.Embarked.fillna('S')\n", + "\n", + "\n", + " #Special case for cabins as nan may be signal\n", + " df.loc[ df.Cabin.isnull()==True,'Cabin'] = 0.5\n", + " df.loc[ df.Cabin.isnull()==False,'Cabin'] = 1.5\n", + " #Fare per person\n", + "\n", + " df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\n", + "\n", + " #Age times class\n", + "\n", + " df['AgeClass']=df['AgeFill']*df['Pclass']\n", + " df['ClassFare']=df['Pclass']*df['Fare_Per_Person']\n", + "\n", + "\n", + " df['HighLow']=df['Pclass']\n", + " df.loc[ (df.Fare_Per_Person<8) ,'HighLow'] = 'Low'\n", + " df.loc[ (df.Fare_Per_Person>=8) ,'HighLow'] = 'High'\n", + " \n", + " #df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n", + "\n", + " le.fit(df['Sex'] )\n", + " x_sex=le.transform(df['Sex'])\n", + " df['Sex']=x_sex.astype(np.float)\n", + "\n", + " le.fit( df['Ticket'])\n", + " x_Ticket=le.transform( df['Ticket'])\n", + " df['Ticket']=x_Ticket.astype(np.float)\n", + "\n", + " le.fit(df['Title'])\n", + " x_title=le.transform(df['Title'])\n", + " df['Title'] =x_title.astype(np.float)\n", + "\n", + " le.fit(df['HighLow'])\n", + " x_hl=le.transform(df['HighLow'])\n", + " df['HighLow']=x_hl.astype(np.float)\n", + "\n", + "\n", + " le.fit(df['AgeCat'])\n", + " x_age=le.transform(df['AgeCat'])\n", + " df['AgeCat'] =x_age.astype(np.float)\n", + "\n", + " le.fit(df['Embarked'])\n", + " x_emb=le.transform(df['Embarked'])\n", + " df['Embarked']=x_emb.astype(np.float)\n", + "\n", + " df = df.drop(['Name','Age','Cabin'], axis=1) #remove Name,Age and PassengerId\n", + "\n", + "\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the cleaning function to clean the data" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1 800\n", + " 14 8\n", + " 149 7\n", + " 63 6\n", + " 50 6\n", + " 59 6\n", + " 17 5\n", + " 384 4\n", + " 27 4\n", + " 25 4\n", + " 162 4\n", + " 8 4\n", + " 84 4\n", + " 340 4\n", + " 43 3\n", + " 269 3\n", + " 58 3\n", + " 633 2\n", + " 167 2\n", + " 280 2\n", + " 510 2\n", + " 90 2\n", + " 83 1\n", + " 625 1\n", + " 376 1\n", + " 449 1\n", + " 498 1\n", + " 588 1\n", + "dtype: int64\n", + "-1 384\n", + " 149 4\n", + " 25 3\n", + " 280 3\n", + " 27 2\n", + " 59 2\n", + " 633 2\n", + " 510 2\n", + " 167 2\n", + " 90 2\n", + " 162 1\n", + " 759 1\n", + " 449 1\n", + " 84 1\n", + " 269 1\n", + " 58 1\n", + " 43 1\n", + " 794 1\n", + " 918 1\n", + " 17 1\n", + " 14 1\n", + " 8 1\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "df=clean_and_munge_data(rawdf)\n", + "df_test=clean_and_munge_data(rawdf_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassSexSibSpParchTicketFareEmbarkedFamily_SizeFamilyTitleFamilyIdAgeFillAgeCatFare_Per_PersonAgeClassClassFareHighLow
count891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000891.000000
mean446.0000000.3838382.3086420.6475870.5230080.381594338.52862032.6893181.5364760.9046020.5679011.86083114.23232329.8191311.59147020.40148665.06247732.1188520.417508
std257.3538420.4865920.8360710.4779901.1027430.806057200.85065749.6116390.7915031.6134591.9792870.72106669.88636813.2854231.42895235.89441333.67629535.8452100.493425
min1.0000000.0000001.0000000.0000000.0000000.0000000.0000004.0125000.0000000.0000000.0000000.000000-1.0000000.4200000.0000001.1321430.9200003.3964290.000000
25%223.5000000.0000002.0000000.0000000.0000000.000000158.5000007.9250001.0000000.0000000.0000002.000000-1.00000021.8356160.0000007.58960039.50000021.5458830.000000
50%446.0000000.0000003.0000001.0000000.0000000.000000337.00000014.5000002.0000000.0000000.0000002.000000-1.00000030.0000002.0000008.66250063.00000024.1500000.000000
75%668.5000001.0000003.0000001.0000001.0000000.000000519.50000031.2750002.0000001.0000000.0000002.000000-1.00000035.8416673.00000024.50000091.75000028.5000001.000000
max891.0000001.0000003.0000001.0000008.0000006.000000680.000000512.3292002.00000010.00000016.0000003.000000633.00000080.0000003.000000512.329200222.000000512.3292001.000000
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Sex SibSp \\\n", + "count 891.000000 891.000000 891.000000 891.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 0.647587 0.523008 \n", + "std 257.353842 0.486592 0.836071 0.477990 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.000000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 0.000000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 1.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 1.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 1.000000 8.000000 \n", + "\n", + " Parch Ticket Fare Embarked Family_Size \\\n", + "count 891.000000 891.000000 891.000000 891.000000 891.000000 \n", + "mean 0.381594 338.528620 32.689318 1.536476 0.904602 \n", + "std 0.806057 200.850657 49.611639 0.791503 1.613459 \n", + "min 0.000000 0.000000 4.012500 0.000000 0.000000 \n", + "25% 0.000000 158.500000 7.925000 1.000000 0.000000 \n", + "50% 0.000000 337.000000 14.500000 2.000000 0.000000 \n", + "75% 0.000000 519.500000 31.275000 2.000000 1.000000 \n", + "max 6.000000 680.000000 512.329200 2.000000 10.000000 \n", + "\n", + " Family Title FamilyId AgeFill AgeCat \\\n", + "count 891.000000 891.000000 891.000000 891.000000 891.000000 \n", + "mean 0.567901 1.860831 14.232323 29.819131 1.591470 \n", + "std 1.979287 0.721066 69.886368 13.285423 1.428952 \n", + "min 0.000000 0.000000 -1.000000 0.420000 0.000000 \n", + "25% 0.000000 2.000000 -1.000000 21.835616 0.000000 \n", + "50% 0.000000 2.000000 -1.000000 30.000000 2.000000 \n", + "75% 0.000000 2.000000 -1.000000 35.841667 3.000000 \n", + "max 16.000000 3.000000 633.000000 80.000000 3.000000 \n", + "\n", + " Fare_Per_Person AgeClass ClassFare HighLow \n", + "count 891.000000 891.000000 891.000000 891.000000 \n", + "mean 20.401486 65.062477 32.118852 0.417508 \n", + "std 35.894413 33.676295 35.845210 0.493425 \n", + "min 1.132143 0.920000 3.396429 0.000000 \n", + "25% 7.589600 39.500000 21.545883 0.000000 \n", + "50% 8.662500 63.000000 24.150000 0.000000 \n", + "75% 24.500000 91.750000 28.500000 1.000000 \n", + "max 512.329200 222.000000 512.329200 1.000000 " + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Logistic Regression Model. Kaggle Score: (Don't have enough submissions to investigate, probably around 0.76077)." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.792404097151\n", + "148\n" + ] + } + ], + "source": [ + "# The columns we'll use to predict the target\n", + "predictorsLog = [\"Pclass\", \"Sex\", \"AgeCat\", \"AgeFill\", \"Family_Size\",\"Fare_Per_Person\", \"Fare\", \"Embarked\"]\n", + "\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, df[predictorsLog], df[\"Survived\"], cv=10)\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print scores.mean()\n", + "\n", + "# Train the algorithm using all the training data\n", + "alg.fit(df[predictorsLog], df[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictionsLog = alg.predict_proba(df_test[predictorsLog])[:,1]\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": df_test[\"PassengerId\"],\n", + " \"Survived\": np.round(predictionsLog).astype(int)\n", + " })\n", + "print sum(submission.Survived)\n", + "submission.to_csv(\"kaggleLogReg.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an attempt to use a random forest classifier. Results on Kaggle: 0.79904. Without the algorithm's parameters the results were only 0.72727 on Kaggle. Using 5000 estimators and max_depth 10 the kaggle score reduced to 0.77990." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.837261945296\n", + "148\n" + ] + } + ], + "source": [ + "# The columns we'll use to predict the target\n", + "predictorsRan = [\"Pclass\", \"Sex\", \"AgeCat\", \"AgeFill\", \"Family_Size\", \"Fare_Per_Person\", \"Fare\", \"Embarked\", \"Title\", \"ClassFare\", \"FamilyId\"]\n", + "\n", + "# Initialize our algorithm (The parameters are taken from: https://github.com/elenacuoco/kaggle-competitions/blob/master/Titanic-For_Blog.ipynb)\n", + "alg = RandomForestClassifier(n_estimators=350, criterion='entropy', max_depth=5, min_samples_split=2,\n", + " min_samples_leaf=2, max_features='auto', bootstrap=False, oob_score=False, n_jobs=1, random_state=2,\n", + " verbose=0)\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, df[predictorsRan], df[\"Survived\"], cv=10)\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print scores.mean()\n", + "# Train the algorithm using all the training data\n", + "alg.fit(df[predictorsRan], df[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictionsRan = alg.predict_proba(df_test[predictorsRan])[:,1]\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": df_test[\"PassengerId\"],\n", + " \"Survived\": np.round(predictionsRan).astype(int)\n", + " })\n", + "print sum(submission.Survived)\n", + "submission.to_csv(\"kaggleRanFor.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gradient Boost Kaggle Score: 0.78947" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.827212007718\n", + "143\n" + ] + } + ], + "source": [ + "# The columns we'll use to predict the target\n", + "predictorsGrad = [\"Pclass\", \"Sex\", \"AgeFill\", \"Family_Size\", \"Fare\", \"Embarked\", \"Title\", \"FamilyId\"]\n", + "\n", + "# Initialize our algorithm (The parameters are taken from: https://github.com/elenacuoco/kaggle-competitions/blob/master/Titanic-For_Blog.ipynb)\n", + "alg = GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3)\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, df[predictorsGrad], df[\"Survived\"], cv=10)\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print scores.mean()\n", + "# Train the algorithm using all the training data\n", + "alg.fit(df[predictorsGrad], df[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictionsGrad = alg.predict_proba(df_test[predictorsGrad])[:,1]\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": df_test[\"PassengerId\"],\n", + " \"Survived\": np.round(predictionsGrad).astype(int)\n", + " })\n", + "print sum(submission.Survived)\n", + "submission.to_csv(\"kaggleGrad.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create Ensemble Prediction (Logistic regression, Random Forest, GradientBoosting). Overall the Random forest is the best model and so combining it with the others brought down the score to 0.79426 compared to 0.79904 with the Random Forest alone. While I would have hoped that the ensemble would beat the Random Forest alone, it makes sense that the others would bring down the score." + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "149\n" + ] + } + ], + "source": [ + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": df_test[\"PassengerId\"],\n", + " \"Survived\": np.round((3*predictionsRan + predictionsLog + predictionsGrad)/5).astype(int)\n", + " })\n", + "submission.to_csv(\"kaggleEnsem.csv\", index=False)\n", + "print sum(submission.Survived)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the future I would like to try the ensemble method but having each algorithm vote instead of averaging the results. I think it would also be cool if I could analyse the ethnicity of the names and see if that correlated with survival. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/test.csv b/test.csv new file mode 100644 index 0000000..f705412 --- /dev/null +++ b/test.csv @@ -0,0 +1,419 @@ +PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q +893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S +894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q +895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S +896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S +897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S +898,3,"Connolly, Miss. 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