diff --git a/data_exporation.ipynb b/data_exporation.ipynb new file mode 100644 index 0000000..e3f59a5 --- /dev/null +++ b/data_exporation.ipynb @@ -0,0 +1,527 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Titanic Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "\n", + "cur_dir = os.path.dirname('__file__')\n", + "\n", + "train = pd.read_csv(os.path.join(cur_dir, \"train.csv\"))\n", + "test = pd.read_csv(os.path.join(cur_dir, \"test.csv\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Class vs Number Survivors" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.countplot(x=\"Pclass\", hue=\"Survived\", data=train.sort_values(\"Pclass\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 1st class had the best proportion. Third class was the most common." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sex vs. Survival" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.countplot(x=\"Sex\", hue=\"Survived\", data=train.sort_values(\"Pclass\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nearly all men died, and most women survived." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def is20(n):\n", + " if pd.isnull(n):\n", + " return n\n", + " return n > 20\n", + "\n", + "is20 = train.Age.apply(is20)\n", + "is20.name='is20'\n", + "train['is20'] = is20\n", + "\n", + "ax = sns.countplot(x=is20, hue=\"Survived\", data=train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Children below 20 years old had an even chance at survival, most adults died." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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uezh27OiUlPs3v/m36N3bLkYW1lj1fJWv1Q0f+CDxeIKnnhpl2PRr2xprFW2Y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\nILtffAIAu69I3GxGiWuoiopaEALpOx5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(data=train, x=\"Age\", y=\"Survived\", orient=\"h\")\n", + "ax = sns.stripplot(x=\"Age\", y=\"Survived\", data=train, size=4, jitter=True, edgecolor=\"gray\", orient=\"h\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The age distribution for survivors was slightly lower than those who died." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## By Age and Gender" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sex is20 \n", + "female False 0.688312\n", + " True 0.782609\n", + "male False 0.284314\n", + " True 0.182336\n", + "Name: Survived, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print train.groupby(['Sex', 'is20'])['Survived'].mean()\n", + "ax = sns.barplot(x=\"Sex\", y=\"Survived\", hue=\"is20\", data=train.sort_values('is20'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interesting. Females survived regardless of their age (in fact, it seems older women faired better.) For males, much more under 20 boys survived." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bVFWdBXwIhOJ+wkmtSD+TNEcKH9Ztpqqr+pT2kRIXwXdvXESy084bG6t45q1S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\nVtW7eHyeUIcjhJjiJClMcpEWB+emL6f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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from statsmodels.nonparametric.smoothers_lowess import lowess\n", + "\n", + "male = train[train.Sex == 'male']\n", + "female = train[train.Sex == 'female']\n", + "\n", + "# sns.regplot(data=train, x='Age', y='Survived', lowess=True) # this makes a pretty line\n", + "# sns.regplot(data=male, x='Age', y='Survived', lowess=True) # this makes a flat line at 0.\n", + "\n", + "# I also tried manually doing the regression, which is what seaborn does anyway.\n", + "\n", + "l1 = lowess(train.Survived, train.Age, it=1)\n", + "plt.plot(l1[:, 0], l1[:, 1]) # pretty line!\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Survival Rate')\n", + "\n", + "l2 = lowess(male.Survived, male.Age, it=1)\n", + "plt.plot(l2[:, 0], l2[:, 1]) # flat line. :(\n", + "\n", + "l3 = lowess(female.Survived, female.Age, it=1)\n", + "plt.plot(l3[:, 0], l3[:, 1]) # flat line. :(\n", + "\n", + "plt.legend(['Cumulative', 'Male', 'Female'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a graph of survival rate vs age, smoothed out using LOWESS. We can see survival rate drops rapily from ages 0-20, with infants being very likely to survive and 20-year-olds have a 35% chance. From age 20-50 it is realtively even, and after age 50 it continues dropping off." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def getAgeGroup(n):\n", + " if pd.isnull(n):\n", + " return n\n", + " if n < 20:\n", + " return '0-20'\n", + " elif n < 50:\n", + " return '20-50'\n", + " else:\n", + " return \"50-99\"\n", + " \n", + "train['age_group'] = train.Age.apply(getAgeGroup)\n", + "\n", + "ax = sns.barplot(x='Sex', y='Survived',hue=\"age_group\", data=train.sort_values('age_group'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This confirms our earlier findings that younger men and older women survived." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass Sex \n", + "1 female 0.968085\n", + " male 0.368852\n", + "2 female 0.921053\n", + " male 0.157407\n", + "3 female 0.500000\n", + " male 0.135447\n", + "Name: Survived, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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ID3QGILJNhZk9m3idT/xhLw8BV7n73wDcfT5s7QPoVA0sNLN8YATb+hF+Biwz\ns18Dj7v7O2bWAnzLzB4CngR+GvLPJNIjnQGIbFPl7tMSf6a6+3VAByn+PzGzQuBXwEWJb/p3k7g8\n5O5XAqcQv2z0hJmd6O4OHEh87vnjgOdC/HlEUlIAiKTg7huAajM7FMDMrjKzGWy7BDQEaAfWJGaX\nPBXYzcx2N7MbgA/d/T7gHmCimf0HMNHdnwEuAcYkzhxEMk6XgES26enOnnOA+WbWCtQmlv8diLl7\nrZk9CvwR+JD4swF+TvzbfQnwRzOrJd6BPB0YDtxnZluInync6u4dIf5MIj3SdNAiIhGlU08RkYhS\nAIiIRJQCQEQkohQAIiIRpQAQEYkoBYCISEQpAEREIkoBICISUf8HE58ItS6/83MAAAAASUVORK5C\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print train.groupby(['Pclass', 'Sex'])['Survived'].mean()\n", + "ax = sns.barplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=train.sort_values(\"Pclass\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The people in the higher classes survived." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass age_group\n", + "1 0-20 0.809524\n", + " 20-50 0.702479\n", + " 50-99 0.454545\n", + "2 0-20 0.742857\n", + " 20-50 0.428571\n", + " 50-99 0.315789\n", + "3 0-20 0.333333\n", + " 20-50 0.203390\n", + " 50-99 0.090909\n", + "Name: Survived, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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hY7Kt43EPcKG7TwX+BlxGcEKKsq7kx5vAZe4+HTgF+DlQ1mn+tn7/3wG+ZmYL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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print train.groupby(['Pclass', 'age_group'])['Survived'].mean()\n", + "ax = sns.barplot(x=\"age_group\", y=\"Survived\", hue=\"Pclass\", data=train.sort_values(\"Pclass\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems age has the biggest effect on the droppoff in the middle class from 0-20 to 20-50." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SRLygue5U4NoStTwpzX/G6cx/RvuvV5KqVeaGMGe1r4uIC0s8b0NEbIyI9RSnel4WERdR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\nQJJqygCQpJr6/wen8UCcBmRcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def trans_sibsp(n):\n", + " if n > 1:\n", + " return 2\n", + " else:\n", + " return n\n", + "\n", + "# ax = sns.barplot(x=\"Parch\", y=\"Survived\", data=train.sort_values('Parch'))\n", + "# ax = sns.barplot(x=train.Parch > 0, y=train[\"Survived\"])\n", + "ax = sns.barplot(x=train.Embarked, y=train[\"Survived\"])\n", + "# ax = sns.barplot(x=(train.SibSp.apply(trans_sibsp)), y=train.Survived)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from statsmodels.nonparametric.smoothers_lowess import lowess\n", + "\n", + "\n", + "# sns.regplot(data=train, x='Age', y='Survived', lowess=True) # this makes a pretty line\n", + "# sns.regplot(data=male, x='Age', y='Survived', lowess=True) # this makes a flat line at 0.\n", + "\n", + "# I also tried manually doing the regression, which is what seaborn does anyway.\n", + "\n", + "l1 = lowess(train.Survived, train.Fare, it=1)\n", + "plt.plot(l1[:, 0], l1[:, 1]) # pretty line!\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Survival Rate')" + ] + }, + { + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_1.ipynb b/model_iteration_1.ipynb new file mode 100644 index 0000000..3a28654 --- /dev/null +++ b/model_iteration_1.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import basic modules we will use and load in the train and test set" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import sklearn\n", + "# import statsmodels.api as sm\n", + "\n", + "%matplotlib inline\n", + "\n", + "cur_dir = os.path.dirname('__file__')\n", + "\n", + "train = pd.read_csv(os.path.join(cur_dir, \"train.csv\"))\n", + "test = pd.read_csv(os.path.join(cur_dir, \"test.csv\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocess the data. In this case, we fill all `NaN` values in the `Sex` and `Age` columns. We also create dummy variables out of the categorical columns `Pclass` and `Embarked`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "target = \"Survived\"\n", + "\n", + "def preprocess(df):\n", + "\n", + " df.Sex = (df.Sex == \"male\").astype(int)\n", + " df.Age = df.Age.fillna(df.Age.mean())\n", + " df.Embarked = df.Embarked.fillna(df.Embarked.mode())\n", + " df[\"oneSibSp\"] = (df.SibSp == 1).astype(int)\n", + " df[\"hasParch\"] = (df.Parch > 0).astype(int)\n", + " df[\"maleAge\"] = df.Sex * df.Age\n", + " df[\"is20\"] = df.Age >= 20\n", + "\n", + " dummy_class = pd.get_dummies(df['Pclass'], prefix='class')\n", + " dummy_emb = pd.get_dummies(df['Embarked'], prefix='emb')\n", + " df = pd.concat([df, dummy_class, dummy_emb], axis=1)\n", + " \n", + " return df\n", + "\n", + "train = preprocess(train)\n", + "test = preprocess(test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### This is some `statsmodels` `LogisticRegression` code. I ended up using `sklearn` instead because of its easy to use `cross_validation` module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "# from statsmodels.tools.sm_exceptions import PerfectSeparationError\n", + "\n", + "# # baseline_cols = [\"Sex\", \"Age\", \"class_2\", \"class_3\", \"intercept\"]\n", + "# baseline_cols = [\"Sex\", \"Age\", \"class_2\", \"class_3\", \"intercept\"]\n", + "# t = []\n", + "# for name in df.columns:\n", + "# if name == \"Survived\" or name in baseline_cols:\n", + "# continue\n", + "\n", + "# independents = baseline_cols + [name]\n", + "\n", + "# # for i in independents:\n", + "# # print df[i].dtype\n", + "\n", + "# logit = sm.Logit(df[\"Survived\"], df[baseline_cols + [name]], missing='drop')\n", + "# result = logit.fit()\n", + "\n", + "# print result.prsquared\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's do some data mining. We assume that age, sex, and class are all good predictors. Let's check if any of the other variables give us any additional insight beyond these three features." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added predictiveness beyond Age, class, and sex:\n", + "\n", + "maleAge: 0.45% extra accuracy on predictions\n", + "emb_Q: 0.34% extra accuracy on predictions\n", + "is20: 0.22% extra accuracy on predictions\n", + "emb_S: 0.00% extra accuracy on predictions\n", + "oneSibSp: -0.11% extra accuracy on predictions\n", + "Fare: -0.45% extra accuracy on predictions\n", + "hasParch: -0.56% extra accuracy on predictions\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn import cross_validation\n", + "\n", + "alg = LogisticRegression(random_state=1)\n", + "\n", + "scores = []\n", + "\n", + "possible_predictors = [\"Sex\", \"Age\", \"is20\", \"Fare\", \"oneSibSp\", \"hasParch\", \"maleAge\", \"class_2\", \"class_3\",\n", + " \"emb_Q\", \"emb_S\"]\n", + "\n", + "baseline_cols = [\"Sex\", \"Age\", \"class_2\", \"class_3\"]\n", + "baseline_score = cross_validation.cross_val_score(alg, train[baseline_cols], train[target], cv=3).mean()\n", + "\n", + "for name in possible_predictors:\n", + " if name == \"Survived\" or name in baseline_cols:\n", + " continue\n", + "\n", + " score = cross_validation.cross_val_score(alg, train[baseline_cols + [name]], train[target], cv=3).mean()\n", + " scores.append((name, score))\n", + "\n", + " \n", + "scores = sorted(scores, key=lambda x: x[1], reverse=True) \n", + "\n", + "print \"Added predictiveness beyond Age, class, and sex:\"\n", + "print\n", + "for var, score in scores:\n", + " print \"%s: %0.2f%% extra accuracy on predictions\" % (var, (score - baseline_score) * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## So adjusting for age's effects based on Sex seems to help a bit, and there might be something special about the age 20. I chose to omit all other features from the final model because they added very little benefit and I'm not convinced it was due to anything other than noise." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.79012345679012341" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictors = [\"Sex\", \"Age\", \"class_2\", \"class_3\", \"maleAge\", \"is20\"]\n", + "score = cross_validation.cross_val_score(alg, train[predictors], train[target], cv=3).mean(); score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 79% accuracy isn't bad! Let's check what the baseline accuracy is:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.61616161616161613" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 - train['Survived'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate a submission" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "alg = LogisticRegression(random_state=1)\n", + "\n", + "# Train the algorithm using all the training data\n", + "alg.fit(train[predictors], train[target])\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", + "submission = pd.DataFrame({\n", + " \"PassengerId\": test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "\n", + "submission.to_csv(\"kaggle.csv\", index=False)" + ] + }, + { + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_2.ipynb b/model_iteration_2.ipynb new file mode 100644 index 0000000..0bd7407 --- /dev/null +++ b/model_iteration_2.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I drew inspiration from both the DataQuest about forests and the blog post on altervista" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import sklearn\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "\n", + "cur_dir = os.path.dirname('__file__')\n", + "\n", + "train = pd.read_csv(os.path.join(cur_dir, \"train.csv\"))\n", + "test = pd.read_csv(os.path.join(cur_dir, \"test.csv\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This cell contains code to clean up the data for a submission. It creates a few features, such as `oneSibSp` and `hasParch`. Additionally, it fills NaNs and extracts title information from the people's names." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import re, operator, string\n", + "\n", + "# A function to get the title from a name.\n", + "def get_title(name):\n", + " # Use a regular expression to search for a title. 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 return it.\n", + " if title_search:\n", + " return title_search.group(1)\n", + " return \"\"\n", + "\n", + "# A function to get the id given a row\n", + "def get_family_id(row, family_id_mapping):\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[\"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]\n", + "\n", + "def preprocess(df):\n", + " \n", + " df = df.copy()\n", + "\n", + " df.Sex = (df.Sex == \"male\").astype(int)\n", + " df.Age = df.Age.fillna(df.Age.median())\n", + " df.Embarked = df.Embarked.fillna(df.Embarked.mode())\n", + " df.Fare = df.Fare.fillna(df.Fare.median())\n", + " df[\"oneSibSp\"] = (df.SibSp == 1).astype(int)\n", + " df[\"hasParch\"] = (df.Parch > 0).astype(int)\n", + " df[\"maleAge\"] = df.Sex * df.Age\n", + " df[\"is20\"] = df.Age >= 20\n", + " # Generating a familysize column\n", + " df[\"FamilySize\"] = df[\"SibSp\"] + df[\"Parch\"]\n", + " \n", + " # Get all the titles and print how often each one occurs.\n", + " titles = df[\"Name\"].apply(get_title)\n", + "\n", + " # Map each title to an integer. 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, \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Lady\": 10, \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\n", + " for k,v in title_mapping.items():\n", + " titles[titles == k] = v\n", + " # Add in the title column.\n", + " df[\"Title\"] = titles\n", + "\n", + " # The .apply method generates a new series\n", + " df[\"NameLength\"] = df[\"Name\"].apply(lambda x: len(x))\n", + " \n", + " # A dictionary mapping family name to id\n", + " family_id_mapping = {}\n", + " \n", + " # Get the family ids with the apply method\n", + " family_ids = df.apply(lambda x:get_family_id(x, family_id_mapping), 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[\"FamilySize\"] < 3] = -1\n", + "\n", + " df[\"FamilyId\"] = family_ids\n", + "\n", + " dummy_class = pd.get_dummies(df['Pclass'], prefix='class')\n", + " dummy_emb = pd.get_dummies(df['Embarked'], prefix='emb')\n", + " df = pd.concat([df, dummy_class, dummy_emb], axis=1)\n", + " \n", + " return df\n", + "\n", + "processed = preprocess(train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This cell is also pre-processing code, but it is strongly based on the altervista approach. NaNs are filled in smartly, attempting to guess `Age` based on `Title`, and `Fare` based on `Class`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn import preprocessing\n", + "import string\n", + "\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", + "le = preprocessing.LabelEncoder()\n", + "enc=preprocessing.OneHotEncoder()\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", + "\n", + "# This is code from http://elenacuoco.altervista.org/blog/archives/1195\n", + "def clean_and_munge_data(df):\n", + " \n", + " df = df.copy()\n", + " \n", + " #setting silly values to nan\n", + " df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\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", + " df['Title']=df.apply(replace_titles, axis=1)\n", + " df['Title_Name'] = df['Title']\n", + "\n", + " #Creating new family_size column\n", + " df['Family_Size']=df['SibSp']+df['Parch']\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['Gender'] = df['Sex'].apply( lambda x: {'female': 0, 'male': 1}[x] ).astype(int)\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", + " df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\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['Title'])\n", + " x_title=le.transform(df['Title'])\n", + " df['Title'] =x_title.astype(np.float)\n", + "\n", + " le.fit(df['AgeCat'])\n", + " x_age=le.transform(df['AgeCat'])\n", + " df['AgeCat'] =x_age.astype(np.float)\n", + "\n", + " df = df.drop(['PassengerId','Name','Cabin'], axis=1) #remove Name,Age and PassengerId\n", + "\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## There's some pretty neat code here to add a `Title` column. Let's see if it actually has any predictive value:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lICuqQySjjZV7BHgbgKTnSFqedYcR0QFcB3yO3l4fTyC5SQxwKUn/8ll0A+uA\nN0g6Lq3nPZLOrLyZWXYOACuCo3V5+3+AWZLup293wO8DLk678b0bGOgGcJ/9R8R3SYYX7HET8E1J\na0je0NvSUaTKu7o+osaI2EhyQ/ohSQ8D5wC/zFCPWSbuDtrMrKB8D8BsAJLeTXpZqMxTEfGX1a7H\nbKT4DMDMrKB8D8DMrKAcAGZmBeUAMDMrKAeAmVlBOQDMzArKAWBmVlD/H4UuQndkUrhKAAAAAElF\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = clean_and_munge_data(train)\n", + "ax = sns.barplot(x=\"Title_Name\", y=\"Survived\", data=df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cool. As we'll see below, this has a lot to do with the age and gender of the people with particular titles. Mr's are older men, who all died. Masters are young boys." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's do some quick validation on our NaN filling:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IpUBPZu4CyMwNEXFQRCyhCrCnAGc2LDv4qOsdwJuAzwGnA7eNpsDe3h2jaTas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\nKopBVpIkSZJUFIOsJEmSJKk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = clean_and_munge_data(train)\n", + "bins = np.linspace(0, 300, 15)\n", + "f, axtuple = plt.subplots(1, 3, sharey=True)\n", + "\n", + "for c in [1, 2, 3]:\n", + " \n", + " f = df[df.Pclass == c].Fare.reset_index(drop=True)\n", + " axtuple[c-1].hist(f, bins, label=\"%s class\" % {1: \"First\", 2: \"Second\", 3:\"Third\"}[c], normed=True)\n", + " axtuple[c-1].set_title(\"%s class\" % {1: \"First\", 2: \"Second\", 3:\"Third\"}[c])\n", + " \n", + "plt.gcf().set_size_inches(16, 6, forward=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `Pclass` clearly was a good indicator of `Fare`, and using it to fill NaNs was smart. Let's do the same with `Title` and `Age`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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S4QxrSZIKZ1hLklQ4w1qSpMIZ1pIkFc6wliSpcMc1WyAiNgMXADPAxsx8pG7e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\nWEuSVDjDWpKkwhnWkiQVzrCWJKlw/xsxqrLbqshf+wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = clean_and_munge_data(train)\n", + "bins = np.linspace(0, 80, 15)\n", + "f, axtuple = plt.subplots(2, 2, sharey=True, sharex=True)\n", + "\n", + "for i, t in enumerate(['Miss', 'Mrs', 'Mr', 'Master']):\n", + " \n", + " f = df[df.Title_Name == t].Age.reset_index(drop=True)\n", + " axtuple[i / 2, i % 2].hist(f, bins, normed=True)\n", + " axtuple[i / 2, i % 2].set_title(t)\n", + " \n", + "plt.gcf().set_size_inches(8, 8, forward=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Also some valuable info here. Masters are very young, Miss are younger than Mrs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Here's a machine learning algorithm using a RandomForest and featues that I engineered (using the `preprocess` function I wrote, as opposed to the `clean_and_munge_data` function)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Algorithm score: 0.828\n" + ] + } + ], + "source": [ + "from sklearn import cross_validation\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "titanic = preprocess(train)\n", + "\n", + "# predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "predictors = [\"Sex\", \"Age\", \"SibSp\", \"hasParch\", \"Fare\", \"class_2\", \"class_3\", \"emb_Q\", \"emb_S\"]\n", + "\n", + "# Initialize our algorithm with the default paramters\n", + "# n_estimators is the number of trees we want to make\n", + "# min_samples_split is the minimum number of rows we need to make a split\n", + "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)\n", + "\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n", + "\n", + "print \"Algorithm score: %.3f\" % scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cool. Let's do some ensembling! First, a utility function to do easy ensemble prediction generation:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_ensemble_prediction(algorithms, weights, train_df, test_df):\n", + " \n", + " train_target = train_df[\"Survived\"]\n", + " full_test_predictions = []\n", + " for alg, formula in algorithms:\n", + " y_train, x_train = dmatrices(formula, data=train_df, return_type='dataframe')\n", + " y_train = np.asarray(y_train).ravel()\n", + " _, x_test = dmatrices(formula, data=test_df, return_type='dataframe')\n", + " alg.fit(x_train, y_train)\n", + " test_predictions = alg.predict_proba(x_test)[:,1]\n", + " full_test_predictions.append(test_predictions)\n", + " test_predictions.fill(0)\n", + " for i, w in enumerate(weights):\n", + " test_predictions += w * full_test_predictions[i]\n", + " \n", + " test_predictions[test_predictions <= .5] = 0\n", + " test_predictions[test_predictions > .5] = 1\n", + " return test_predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Algorithm score: 0.809\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from patsy import dmatrices\n", + "\n", + "titanic = preprocess(train)\n", + "\n", + "# The algorithms we want to ensemble.\n", + "# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.\n", + "algorithms = [\n", + " [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3),\n", + " 'Survived~class_2+class_3+Sex+Age+Fare+emb_Q+emb_S+FamilySize'],\n", + " [LogisticRegression(random_state=1), 'Survived~class_2+class_3+Sex+Age+Fare+emb_Q+emb_S+FamilySize']\n", + "]\n", + "\n", + "weights = [.5, .5]\n", + "\n", + "# Initialize the cross validation folds\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train_set, test_set in kf:\n", + " test_predictions = get_ensemble_prediction(algorithms, weights, titanic.iloc[train_set, :], titanic.iloc[test_set, :])\n", + " predictions.append(test_predictions)\n", + " \n", + "# Put all the predictions together into one array.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "# Compute accuracy by comparing to the training data.\n", + "accuracy = (predictions == titanic[\"Survived\"]).mean()\n", + "print \"Algorithm score: %.3f\" % accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Not bad! Let's do a different model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is a random forest I got inspiration from altervista" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Algorithm score: 0.824\n" + ] + } + ], + "source": [ + "from patsy import dmatrices\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn import cross_validation\n", + "\n", + "titanic = clean_and_munge_data(train)\n", + "formula_ml = 'Survived~C(Pclass)+C(Title)+C(AgeCat)*Sex+Fare_Per_Person+Fare+Family_Size'\n", + "y_train, x_train = dmatrices(formula_ml, data=titanic, return_type='dataframe')\n", + "y_train = np.asarray(y_train).ravel()\n", + "\n", + "# X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size=0.2)\n", + "\n", + "alg=RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1,\n", + " min_samples_leaf=1, bootstrap=False, oob_score=False)\n", + "\n", + "scores = cross_validation.cross_val_score(alg, x_train, y_train, cv=3)\n", + "\n", + "print \"Algorithm score: %.3f\" % scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's now try doing a similar algorithm, but using a GradientBoostingClassifier and a Logistic Regression." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Algorithm score: 0.827\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from patsy import dmatrices\n", + "\n", + "titanic = clean_and_munge_data(train)\n", + "\n", + "# The algorithms we want to ensemble.\n", + "# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.\n", + "algorithms = [\n", + " [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), 'Survived~C(Pclass)+C(Title)+C(AgeCat)*Sex+Fare_Per_Person+Fare+Family_Size'],\n", + " [LogisticRegression(random_state=1), 'Survived~C(Pclass)+Fare+Family_Size+AgeFill*Sex']\n", + "]\n", + "\n", + "# Initialize the cross validation folds\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train_set, test_set in kf:\n", + " test_predictions = get_ensemble_prediction(algorithms, [0.7, 0.3], titanic.iloc[train_set, :], titanic.iloc[test_set, :])\n", + " predictions.append(test_predictions)\n", + " \n", + "# Put all the predictions together into one array.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "# Compute accuracy by comparing to the training data.\n", + "accuracy =(predictions == titanic[\"Survived\"]).mean()\n", + "print \"Algorithm score: %.3f\" % accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate the submission" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "algorithms = [\n", + " [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), 'Survived~C(Pclass)+C(Title)+C(AgeCat)*Sex+Fare_Per_Person+Fare+Family_Size'],\n", + " [LogisticRegression(random_state=1), 'Survived~C(Pclass)+Fare+Family_Size+AgeFill*Sex']\n", + "]\n", + "\n", + "to_train = clean_and_munge_data(train)\n", + "to_test = clean_and_munge_data(test)\n", + "to_test[\"Survived\"] = 0\n", + "\n", + "predictions = get_ensemble_prediction(algorithms, [0.7, 0.3], to_train, to_test)\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": test[\"PassengerId\"],\n", + " \"Survived\": predictions.astype(int)\n", + " })\n", + "\n", + "submission.to_csv(\"kaggle4.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Score: 0.789" + ] + }, + { + "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": "2.7.6" + } + }, + "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 @@ 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Kate",female,30,0,0,330972,7.6292,,Q +899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S +900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C +901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S +902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S +903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S +904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S +905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S +906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S +907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C +908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q +909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C +910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S +911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C +912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C +913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S +914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S +915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C +916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C +917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S +918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C +919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C +920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S +921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C +922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S +923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S +924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S +925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S +926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C +927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C +928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S +929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S +930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S +931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S +932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C +933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S +934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S +935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S +936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S +937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S +938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C +939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q +940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C +941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S +942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S +943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C +944,2,"Hocking, Miss. 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