diff --git a/kaggle/titanic.ipynb b/kaggle/titanic.ipynb index 841956e..b291e14 100644 --- a/kaggle/titanic.ipynb +++ b/kaggle/titanic.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:c2fcf83578d0c021bd7138a746b44b1dfc462c3e8963594da8dc2234fb6f8447" + "signature": "sha256:9f535d1356013ca692a7c44ba8cd310ecd27dd9aca09238616c442798945979a" }, "nbformat": 3, "nbformat_minor": 0, @@ -651,7 +651,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], @@ -1077,6 +1077,556 @@ } ], "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature: Embarked" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Embarked column might be an important feature but is missing a couple data points:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df[df['Embarked'].isnull()]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSex_Val
61 62 1 1 Icard, Miss. Amelie female 38 0 0 113572 80 B28 NaN 0
829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28 NaN 0
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_Val \n", + "61 female 38 0 0 113572 80 B28 NaN 0 \n", + "829 female 62 0 0 113572 80 B28 NaN 0 " + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Determine the unique values of Embarked:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "embarked_locations = sort(df['Embarked'].unique())\n", + "embarked_locations" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 20, + "text": [ + "array([nan, 'C', 'Q', 'S'], dtype=object)" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare to map Embarked from a string to a number representation:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "embarked_locations_mapping = dict(zip(embarked_locations, \n", + " range(0, len(embarked_locations) + 1)))\n", + "embarked_locations_mapping" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "{nan: 0, 'C': 1, 'Q': 2, 'S': 3}" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transform Embarked from a string to a number representation to prepare it for machine learning algorithms:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['Embarked_Val'] = df['Embarked'].map(embarked_locations_mapping).astype(int)\n", + "df.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSex_ValEmbarked_Val
0 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S 1 3
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C 0 1
2 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S 0 3
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S 0 3
4 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 NaN S 1 3
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 22, + "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 Sex_Val Embarked_Val \n", + "0 0 A/5 21171 7.2500 NaN S 1 3 \n", + "1 0 PC 17599 71.2833 C85 C 0 1 \n", + "2 0 STON/O2. 3101282 7.9250 NaN S 0 3 \n", + "3 0 113803 53.1000 C123 S 0 3 \n", + "4 0 373450 8.0500 NaN S 1 3 " + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count the number of passengers by Embarked:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for embarked in range(0, len(embarked_locations)):\n", + " print embarked, len(df[df['Embarked_Val'] == embarked])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0 2\n", + "1 168\n", + "2 77\n", + "3 644\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the histogram for Embarked_Val:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['Embarked_Val'].hist(bins=len(embarked_locations))\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the vast majority of passengers embarked in 'S': 3, we assign the two missing values in Embarked to this: " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df[df['Embarked_Val'] == embarked_locations_mapping[nan]]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSex_ValEmbarked_Val
61 62 1 1 Icard, Miss. Amelie female 38 0 0 113572 80 B28 NaN 0 0
829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28 NaN 0 0
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, + "text": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_Val \\\n", + "61 female 38 0 0 113572 80 B28 NaN 0 \n", + "829 female 62 0 0 113572 80 B28 NaN 0 \n", + "\n", + " Embarked_Val \n", + "61 0 \n", + "829 0 " + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.replace({'Embarked_Val' : { embarked_locations_mapping[nan] : embarked_locations_mapping['S'] }}, inplace=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Verify we do not have any more NaNs for Embarked_Val:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sort(df['Embarked_Val'].unique())" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 27, + "text": [ + "array([1, 2, 3])" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a normalized cross tab for Embarked_Val and Survived:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "embarked_val_xt = pd.crosstab(df['Embarked_Val'], df['Survived'])\n", + "embarked_val_xt_pct = embarked_val_xt.div(embarked_val_xt.sum(1).astype(float), axis=0)\n", + "embarked_val_xt_pct.plot(kind='bar', stacked=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 28, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears those that embarked in location 'C': 1 had the highest rate of survival." + ] } ], "metadata": {}