{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"
\n",
"___\n",
"# Logistic Regression with Python\n",
"\n",
"For this lecture we will be working with the [Titanic Data Set from Kaggle](https://www.kaggle.com/c/titanic). This is a very famous data set and very often is a student's first step in machine learning! \n",
"\n",
"We'll be trying to predict a classification- survival or deceased.\n",
"Let's begin our understanding of implementing Logistic Regression in Python for classification.\n",
"\n",
"We'll use a \"semi-cleaned\" version of the titanic data set, if you use the data set hosted directly on Kaggle, you may need to do some additional cleaning not shown in this lecture notebook.\n",
"\n",
"## Import Libraries\n",
"Let's import some libraries to get started!"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Data\n",
"\n",
"Let's start by reading in the titanic_train.csv file into a pandas dataframe."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv('titanic_train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" | \n",
" PassengerId | \n",
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" Name | \n",
" Sex | \n",
" Age | \n",
" SibSp | \n",
" Parch | \n",
" Ticket | \n",
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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 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 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": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Data Analysis\n",
"\n",
"Let's begin some exploratory data analysis! We'll start by checking out missing data!\n",
"\n",
"## Missing Data\n",
"\n",
"We can use seaborn to create a simple heatmap to see where we are missing data!"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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""
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
},
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5Zc12FngRcAqlTv2CiNibUm9ttdpu4n7DQozmMvOFEXEssGtm3r9Hm5QBz50o9eNXMMzs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\nRagAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small enough for reasonable replacement with some form of imputation. Looking at the Cabin column, it looks like we are just missing too much of that data to do something useful with at a basic level. We'll probably drop this later, or change it to another feature like \"Cabin Known: 1 or 0\"\n",
"\n",
"Let's continue on by visualizing some more of the data! Check out the video for full explanations over these plots, this code is just to serve as reference."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"sns.countplot(x='Survived',data=train,palette='RdBu_r')"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Ak8nEJ598YlgwERHxLa8KYdOmTZSWltKrV9e5FaKIiFwfr84y6tu3L5GRkUZnERERP/Jq\nDyE6OpqMjAzuu+8+goODPcu/f6D5v7W0tDB37lzq6upwOp1kZ2czcOBAZs+eTVBQELGxseTn5wOw\nceNGNmzYgMViITs7m1GjRrXvrxIRkevmVSH06dOHPn36XNfA27Zto2fPnixbtoxz587x6KOPMnjw\nYHJyckhISCA/P5/S0lKGDx9OcXExJSUlXLhwgfT0dJKSkrBYLDf0B4mIyI3xqhCutidwJampqaSk\npABw8eJFunXrxtGjR0lISABg5MiR7N27l6CgIGw2G2azmfDwcKKjo6msrCQuLu66X1NERG6cV4Uw\nePBgTCbTJctuu+02du/efcXnhISEAGC325k+fTozZsxg6dKlnsfDwsKw2+04HA6sVqtneWhoqC56\nExHxA68K4fPPP/f87HQ6KS0t5eDBg9d83smTJ3nxxRfJzMzkoYce4o033vA85nA4iIiIIDw8HLvd\nftlyb5SXl3u1nvhHbW0t/fwd4gZUVFToQ4kEJK8K4fssFgupqan89re/vep6DQ0NTJ06lVdeeYXE\nxEQAhgwZwoEDB7j33nvZs2cPiYmJxMfHU1BQQHNzM01NTVRXVxMbG+tVFpvNdr3xxYesVitndm73\nd4zrFhcXx6BBg/wdQ8QQV/sg7VUhvPfee56fXS4Xx48fv+ZB3zVr1nDu3DlWr15NYWEhJpOJ3Nxc\nFi9ejNPpJCYmhpSUFEwmE1lZWWRkZOByucjJybnkTCYREfENrwrhr3/96yW/9+zZk4KCgqs+Jzc3\nl9zc3MuWFxcXX7YsLS2NtLQ0b6KIiIhBvCqEJUuW4HQ6qamp4eLFi8TGxmI2X/e3TSIi0ol5tVWv\nqKjgl7/8JT169KC1tZWGhgYKCwsZNmyY0flERMRHvCqExYsXU1BQ4CmAgwcPsmjRIjZv3mxoOBER\n8R2v5jI6f/78JXsDw4cPp6mpybBQIiLie14VQmRkJKWlpZ7fS0tLdS8EEZGbjFdfGS1atIjnn3/+\nkrOG1q9fb1goERHxPa/2EPbs2UNISAiffvopf/zjH+nVqxdlZWVGZxMRER/yqhA2btzIunXrCA0N\nZfDgwWzZsoW1a9canU1ERHzIq0JwOp2XXJmsqalFRG4+Xh1DSE5O5plnniE1NRWAHTt2MGbMGEOD\niYiIb3lVCDNnzuTDDz/kwIEDmM1mJk2aRHJystHZRETEh7yefyIlJcVzwxsREbn5eHUMQUREbn4q\nBBERAVQIIiLipkIQERFAhSAiIm4qBBERAVQIIiLipkIQERHAB4Vw6NAhsrKyAPj73//OyJEjmTRp\nEpMmTeKDDz4Avps878knn2TixIns2rXL6EgiItIGr69UvhFFRUVs3bqVsLAw4Lt7M0+ZMoXJkyd7\n1mloaKC4uJiSkhIuXLhAeno6SUlJmkBPRMTHDN1DiIqKorCw0PP7kSNH2LVrF5mZmeTl5eFwODh8\n+DA2mw2z2Ux4eDjR0dFUVlYaGUtERNpgaCGMHTuWbt26eX4fNmwYL7/8MmvXruXOO+9k1apV2O12\nrFarZ53Q0FAaGxuNjCUiIm0w9Cuj/5acnOzZ+CcnJ7N48WJGjBiB3W73rONwOIiIiPBqvPLyckNy\nSseora2ln79D3ICKigp9KJGA5NNCmDp1KvPmzSM+Pp79+/dz9913Ex8fT0FBAc3NzTQ1NVFdXU1s\nbKxX49lsNoMTS3tYrVbO7Nzu7xjXLS4ujkGDBvk7hoghrvZB2qeFMH/+fBYtWoTFYqF3794sXLiQ\nsLAwsrKyyMjIwOVykZOTQ3BwsC9jiYgIPiiEfv36sX79egCGDh3KunXrLlsnLS2NtLQ0o6OIiMhV\n6MI0EREBVAgiIuKmQhAREcDHB5VF5NouXrxIVVWVv2Nct5iYmEuuO5KuR4Ug0slUVVXxwFsfY+7d\nda7iaPm6jr9MQafrdnEqBJFOyNy7H5a+Uf6OIQFGxxBERARQIYiIiJsKQUREABWCiIi4qRBERARQ\nIYiIiJtOOxWRgKOL/9qmQhCRgFNVVcXBdXOJ6hPp7yheq60/C+mvGXrxnwpBRAJSVJ9IYvr18neM\nTkXHEEREBFAhiIiImwpBREQAFYKIiLipEEREBPBBIRw6dIisrCwAvvzySzIyMsjMzGTBggWedTZu\n3MiTTz7JxIkT2bVrl9GRRESkDYYWQlFREXl5eTidTgCWLFlCTk4Oa9eupbW1ldLSUhoaGiguLmbD\nhg0UFRWxYsUKz/oiIuI7hhZCVFQUhYWFnt+PHDlCQkICACNHjmTfvn0cPnwYm82G2WwmPDyc6Oho\nKisrjYwlIiJtMLQQxo4de8ll1i6Xy/NzWFgYdrsdh8OB1Wr1LA8NDaWxsdHIWCIi0gafXqkcFPR/\n/eNwOIiIiCA8PBy73X7Zcm+Ul5d3eEbpOLW1tXSduwL/n4qKCr9+KKmtraUrnu/h73+366H3Ztt8\nWghDhw7lwIED3HvvvezZs4fExETi4+MpKCigubmZpqYmqquriY2N9Wo8m81mcOLOoytOxhUWFubv\nCDckLi7OrzeLt1qtUHPUb69/o/z973Y9rFYrZ3Zu93eM69YR/8ZX+yDt00KYNWsW8+bNw+l0EhMT\nQ0pKCiaTiaysLDIyMnC5XOTk5BAcHOzLWF1CVVUVK/93E7dG9fF3FK9VHTvCDIu/U4iItwwvhH79\n+rF+/XoAoqOjKS4uvmydtLQ00tLSjI7S5d0a1YfbBvb3dwyvnar9F3x9zt8xRMRLXe+LShERMYQK\nQUREAN0PQUQ6gKv1IjU1Nf6O4bWamhp6+DtEJ6RCEJF2u3jqX5TYv+HWc1/5O4pXdMJD21QIItIh\nutJJDzrhoW06hiAiIkAA7yF0tQu9ampq4FZ/pxCRm1nAFkJVVRUPvPUx5t5d4wL2C5X/jxee+6G/\nY4jITSxgCwHA3Lsflr5R/o7hlZav6/wdQURucjqGICIigApBRETcVAgiIgKoEERExE2FICIigApB\nRETcVAgiIgKoEERExE2FICIigApBRETc/DJ1xRNPPEF4eDgA/fv3Jzs7m9mzZxMUFERsbCz5+fn+\niCUiEtB8XgjNzc0AvP32255lL7zwAjk5OSQkJJCfn09paSnJycm+jiYiEtB8/pXR559/zvnz55k6\ndSqTJ0/m0KFDHD16lISEBABGjhzJ/v37fR1LRCTg+XwPoXv37kydOpW0tDS++OILnn32WVwul+fx\nsLAwGhsbfR1LRCTg+bwQoqOjiYqK8vzco0cPjh496nnc4XAQERHh61giIgHP54Xw7rvvcuzYMfLz\n86mvr8dut5OUlERZWRkjRoxgz549JCYmejVWeXn5Deeora1FJ1lJWyoqKvy6l6r3plyJ0e9NnxfC\nU089xZw5c8jIyCAoKIjXX3+dHj16kJeXh9PpJCYmhpSUFK/GstlsN5zDarVCzdFrrygBJy4ujkGD\nBvnt9fXelCvpiPfm1T5I+7wQLBYLy5cvv2x5cXGxr6OIiMj3aL9UREQAFYKIiLipEEREBFAhiIiI\nmwpBREQAFYKIiLipEEREBFAhiIiImwpBREQAFYKIiLipEEREBFAhiIiImwpBREQAFYKIiLipEERE\nBFAhiIiImwpBREQAFYKIiLipEEREBFAhiIiIm9nfAf7D5XIxf/58KisrCQ4O5tVXX+XOO+/0dywR\nkYDRafYQSktLaW5uZv369bz00kssWbLE35FERAJKpymE8vJy/ud//geAYcOGUVFR4edEIiKBpdMU\ngt1ux2q1en43m820trb6MZGISGDpNMcQwsPDcTgcnt9bW1sJCjK2r1q+rjN0/I7UcrqeU7W3+DvG\ndfnmn6eoPX3W3zGuS239WXr6OwRd670JXe/9qfdm20wul8tl8Gt4ZceOHXz66acsWbKEgwcPsnr1\nan73u99dcf3y8nIfphMRuXnYbLY2l3eaQvj+WUYAS5YsYcCAAX5OJSISODpNIYiIiH91moPKIiLi\nXyoEEREBVAgiIuKmQhAREUCFENBcLhf5+flMnDiRSZMm8dVXX/k7ksglDh06RFZWlr9jBIxOc2Ga\n+N735486dOgQS5YsYfXq1f6OJQJAUVERW7duJSwszN9RAob2EAKY5o+SziwqKorCwkJ/xwgoKoQA\npvmjpDMbO3Ys3bp183eMgKJCCGD+mD9KRDov/e8PYPfccw+7d+8G4ODBgwwaNMjPiUQup8kUfEcH\nlQPY2LFj2bt3LxMnTgTQTYmkUzKZTP6OEDA0l5GIiAD6ykhERNxUCCIiAqgQRETETYUgIiKACkFE\nRNxUCCIiAqgQRPjwww954oknePTRR5kwYQJ/+MMf2j3m+vXr2bBhQ7vHycrK4sCBA+0eR8QbujBN\nAlp9fT3Lli3jvffeIyIigm+//ZbMzEzuuusuHnzwwRse9z8X+4l0JSoECWhnzpyhpaWF8+fPExER\nQUhICEuXLiU4OJjRo0ezdu1a7rjjDsrKyvjNb35DcXExWVlZ9OjRgxMnTvDII49w6tQp5s2bB8DS\npUvp06cPdrsdgMjISL744ovLHn/66adZuHAhx48fp7W1lWeffZbx48fT3NxMXl4eR44c4Y477uCb\nb77x27+NBB59ZSQBbfDgwYwePZrk5GTS0tJYvnw5LS0t/PCHP7xsyoTv//6jH/2IDz74gIkTJ/LJ\nJ5945tv56KOPePjhhz3rPfTQQ5SWll72+JtvvklcXBzvvvsuxcXFvPnmm/zjH/9g7dq1mEwmtm/f\nTl5eHl9++aUP/hVEvqM9BAl48+fP5+c//zl79+7lz3/+MxMnTuSNN9646nOGDRsGQK9evRg8eDCf\nffYZFouFAQMG8IM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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"sns.countplot(x='Survived',hue='Pclass',data=train,palette='rainbow')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RLhWe7k5EamGpE8Uglt1sAGAymaBT+D0SUSKw1IliEMtutqFgEN+prtb8F7AkFpY6UYyU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\nAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=30)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train['Age'].hist(bins=30,color='darkred',alpha=0.7)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='SibSp',data=train)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train['Fare'].hist(color='green',bins=40,figsize=(8,4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____\n",
"### Cufflinks for plots\n",
"___\n",
" Let's take a quick moment to show an example of cufflinks!"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import cufflinks as cf\n",
"cf.go_offline()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train['Fare'].iplot(kind='hist',bins=30,color='green')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"## Data Cleaning\n",
"We want to fill in missing age data instead of just dropping the missing age data rows. One way to do this is by filling in the mean age of all the passengers (imputation).\n",
"However we can be smarter about this and check the average age by passenger class. For example:\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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T9vnFd+3ale7u7hx88MFJkqVLl2bRokVpa2tLd3d31q5dm3nz5v320wMAQA0URvPll18+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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 7))\n",
"sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the wealthier passengers in the higher classes tend to be older, which makes sense. We'll use these average age values to impute based on Pclass for Age."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def impute_age(cols):\n",
" Age = cols[0]\n",
" Pclass = cols[1]\n",
" \n",
" if pd.isnull(Age):\n",
"\n",
" if Pclass == 1:\n",
" return 37\n",
"\n",
" elif Pclass == 2:\n",
" return 29\n",
"\n",
" else:\n",
" return 24\n",
"\n",
" else:\n",
" return Age"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now apply that function!"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's check that heat map again!"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Let's go ahead and drop the Cabin column and the row in Embarked that is NaN."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train.drop('Cabin',axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" PassengerId | \n",
" Survived | \n",
" Pclass | \n",
" Name | \n",
" Sex | \n",
" Age | \n",
" SibSp | \n",
" Parch | \n",
" Ticket | \n",
" Fare | \n",
" Embarked | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 3 | \n",
" Braund, Mr. Owen Harris | \n",
" male | \n",
" 22.0 | \n",
" 1 | \n",
" 0 | \n",
" A/5 21171 | \n",
" 7.2500 | \n",
" S | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 1 | \n",
" 1 | \n",
" Cumings, Mrs. John Bradley (Florence Briggs Th... | \n",
" female | \n",
" 38.0 | \n",
" 1 | \n",
" 0 | \n",
" PC 17599 | \n",
" 71.2833 | \n",
" C | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 1 | \n",
" 3 | \n",
" Heikkinen, Miss. Laina | \n",
" female | \n",
" 26.0 | \n",
" 0 | \n",
" 0 | \n",
" STON/O2. 3101282 | \n",
" 7.9250 | \n",
" S | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 1 | \n",
" 1 | \n",
" Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n",
" female | \n",
" 35.0 | \n",
" 1 | \n",
" 0 | \n",
" 113803 | \n",
" 53.1000 | \n",
" S | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 0 | \n",
" 3 | \n",
" Allen, Mr. William Henry | \n",
" male | \n",
" 35.0 | \n",
" 0 | \n",
" 0 | \n",
" 373450 | \n",
" 8.0500 | \n",
" S | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Embarked \n",
"0 0 A/5 21171 7.2500 S \n",
"1 0 PC 17599 71.2833 C \n",
"2 0 STON/O2. 3101282 7.9250 S \n",
"3 0 113803 53.1000 S \n",
"4 0 373450 8.0500 S "
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train.dropna(inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Converting Categorical Features \n",
"\n",
"We'll need to convert categorical features to dummy variables using pandas! Otherwise our machine learning algorithm won't be able to directly take in those features as inputs."
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 889 entries, 0 to 890\n",
"Data columns (total 11 columns):\n",
"PassengerId 889 non-null int64\n",
"Survived 889 non-null int64\n",
"Pclass 889 non-null int64\n",
"Name 889 non-null object\n",
"Sex 889 non-null object\n",
"Age 889 non-null float64\n",
"SibSp 889 non-null int64\n",
"Parch 889 non-null int64\n",
"Ticket 889 non-null object\n",
"Fare 889 non-null float64\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(4)\n",
"memory usage: 83.3+ KB\n"
]
}
],
"source": [
"train.info()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sex = pd.get_dummies(train['Sex'],drop_first=True)\n",
"embark = pd.get_dummies(train['Embarked'],drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.concat([train,sex,embark],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" PassengerId | \n",
" Survived | \n",
" Pclass | \n",
" Age | \n",
" SibSp | \n",
" Parch | \n",
" Fare | \n",
" male | \n",
" Q | \n",
" S | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 3 | \n",
" 22.0 | \n",
" 1 | \n",
" 0 | \n",
" 7.2500 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 1 | \n",
" 1 | \n",
" 38.0 | \n",
" 1 | \n",
" 0 | \n",
" 71.2833 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 1 | \n",
" 3 | \n",
" 26.0 | \n",
" 0 | \n",
" 0 | \n",
" 7.9250 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 1 | \n",
" 1 | \n",
" 35.0 | \n",
" 1 | \n",
" 0 | \n",
" 53.1000 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 0 | \n",
" 3 | \n",
" 35.0 | \n",
" 0 | \n",
" 0 | \n",
" 8.0500 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp Parch Fare male Q S\n",
"0 1 0 3 22.0 1 0 7.2500 1.0 0.0 1.0\n",
"1 2 1 1 38.0 1 0 71.2833 0.0 0.0 0.0\n",
"2 3 1 3 26.0 0 0 7.9250 0.0 0.0 1.0\n",
"3 4 1 1 35.0 1 0 53.1000 0.0 0.0 1.0\n",
"4 5 0 3 35.0 0 0 8.0500 1.0 0.0 1.0"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Our data is ready for our model!\n",
"\n",
"# Building a Logistic Regression model\n",
"\n",
"Let's start by splitting our data into a training set and test set (there is another test.csv file that you can play around with in case you want to use all this data for training).\n",
"\n",
"## Train Test Split"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(train.drop('Survived',axis=1), \n",
" train['Survived'], test_size=0.30, \n",
" random_state=101)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training and Predicting"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logmodel = LogisticRegression()\n",
"logmodel.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predictions = logmodel.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's move on to evaluate our model!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check precision,recall,f1-score using classification report!"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.81 0.93 0.86 163\n",
" 1 0.85 0.65 0.74 104\n",
"\n",
"avg / total 0.82 0.82 0.81 267\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test,predictions))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not so bad! You might want to explore other feature engineering and the other titanic_text.csv file, some suggestions for feature engineering:\n",
"\n",
"* Try grabbing the Title (Dr.,Mr.,Mrs,etc..) from the name as a feature\n",
"* Maybe the Cabin letter could be a feature\n",
"* Is there any info you can get from the ticket?\n",
"\n",
"## Great Job!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
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