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arghyadeep99/logistic-regression.ipynb

Created September 6, 2019 19:35
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Logistic Regression.ipynb
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logistic-regression.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Logistic Regression.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/arghyadeep99/7c51e86331b3512ba9684023474ac27f/logistic-regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gpfV-EvZPiP0",
"colab_type": "text"
},
"source": [
"# Logistic Regression\n",
"\n",
"####While we used linear regression to deal with continuous data, that helped in predicting a future value on the basis of past data, logistic regression is different. Logsitic regression is used when the dependent variable is dichotomous (binary). For example: positive-negative, 0-1, pass-fail, benign-malignant, etc. It is assumed that the data for such dichotomous nature would be independent and that there's no correlation of data between two classes.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "avQGnJNbTvNN",
"colab_type": "text"
},
"source": [
"Many will think, why not linear regression? That's because linear data plotted on graph may look something like this:\n",
"\n",
"\n",
"<figure>\n",
"<center>\n",
"<img src='https://drive.google.com/uc?id=192bLivyYTXRLQaJsG8h-8jphieelQ-HX'/>\n",
" </center>\n",
" <center><figcaption><b>Linear Regression to fit dichotomous data</b></figcaption></center>\n",
"</figure>\n",
"\n",
"But what if we are faced with data having outlier points? \n",
"\n",
"<figure>\n",
"<center>\n",
"<img src='https://drive.google.com/uc?id=1Bk_tgAS1gMjEKqh6CVo6iZ3vNYzLf6Hx'/>\n",
" </center>\n",
" <center><figcaption><b>Linear Regression to fit dichotomous data with outliers</b></figcaption></center>\n",
"</figure>\n",
"\n",
"It is clearly visible that the line shifts just because of one outlier. This increases confusion of the model. \n",
"\n",
"Hence, Logistic regression is used."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lPX6x1cRX81E",
"colab_type": "text"
},
"source": [
"In logistic regression, we use logistic functions that are used to plot probabilistic models. \n",
"\n",
"Sigmoid function is a logistic function that's used in logistic regression. This is how it looks: \n",
"\n",
"\n",
"<figure>\n",
"<center>\n",
"<img src='https://drive.google.com/uc?id=1ro0WaW1kszK3InLQNVoKAoFD9K370REO'/>\n",
" </center>\n",
" <center><figcaption><b>Sigmoid function</b></figcaption></center>\n",
"</figure>\n",
"\n",
"### Let's plot this graph using Python."
]
},
{
"cell_type": "code",
"metadata": {
"id": "r0ZJewoXXrKQ",
"colab_type": "code",
"colab": {}
},
"source": [
"import math\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import warnings\n",
"\n",
"warnings.simplefilter('ignore')\n",
"\n",
"def sigmoid(x):\n",
" a = []\n",
" for item in x:\n",
" a.append(1/(1+math.exp(-item)))\n",
" return a"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "v1PijRvKaf2E",
"colab_type": "code",
"outputId": "4213cfe1-17ab-4622-e920-f0e267891e3b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
}
},
"source": [
"x = np.arange(-10., 10., 0.2)\n",
"sig = sigmoid(x)\n",
"plt.plot(x,sig)\n",
"plt.grid(True)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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xaPvgP74FRpS20rR7E03ArhS210Bqbm7u176QacrVd2HNFtZckMFsnji67O4B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\n4i4ikoP+P7xoo1i73lcEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ItbgATl7tE0G",
"colab_type": "text"
},
"source": [
"### As an example, we will start working on the famous Titanic Dataset hosted on Kaggle. This example will also help us understand some data pre processing and how to draw inference and make next steps.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "oykQKWNotUX1",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np \n",
"import pandas as pd \n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import style\n",
"\n",
"# Algorithms\n",
"from sklearn import linear_model\n",
"from sklearn.linear_model import LogisticRegression\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7BHDjA76uPFa",
"colab_type": "code",
"outputId": "314974ac-1b09-4d28-af20-b36c51cddc1e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VKbFxzy8tX_q",
"colab_type": "code",
"colab": {}
},
"source": [
"test_df = pd.read_csv(\"/content/drive/My Drive/ML-DL101 Datasets/test_titanic.csv\")\n",
"train_df = pd.read_csv(\"/content/drive/My Drive/ML-DL101 Datasets/train_titanic.csv\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "e7Pb5nNeu-8Q",
"colab_type": "code",
"outputId": "9b75e656-1665-4e47-b26d-aabf41ed3a8b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
}
},
"source": [
"train_df.info()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "h0UBHi6ivEUe",
"colab_type": "code",
"outputId": "023943f5-8143-4266-b033-f452daf1dd59",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
}
},
"source": [
"train_df.describe()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... SibSp Parch Fare\n",
"count 891.000000 891.000000 891.000000 ... 891.000000 891.000000 891.000000\n",
"mean 446.000000 0.383838 2.308642 ... 0.523008 0.381594 32.204208\n",
"std 257.353842 0.486592 0.836071 ... 1.102743 0.806057 49.693429\n",
"min 1.000000 0.000000 1.000000 ... 0.000000 0.000000 0.000000\n",
"25% 223.500000 0.000000 2.000000 ... 0.000000 0.000000 7.910400\n",
"50% 446.000000 0.000000 3.000000 ... 0.000000 0.000000 14.454200\n",
"75% 668.500000 1.000000 3.000000 ... 1.000000 0.000000 31.000000\n",
"max 891.000000 1.000000 3.000000 ... 8.000000 6.000000 512.329200\n",
"\n",
"[8 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EW2l3yuIvJ2q",
"colab_type": "code",
"outputId": "b658bfb4-84c5-438b-a45e-49339b1dda5f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"train_df.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7h7PKBWuwTAR",
"colab_type": "text"
},
"source": [
"The head of the data gives us an indication of various parameters that need to be converted into numeric form for prediction. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "6LsdW4ojwigB",
"colab_type": "code",
"outputId": "00a0ec08-1dee-4815-eaa1-93c3b6ae3ee4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"total = train_df.isnull().sum().sort_values(ascending=False)\n",
"percent_1 = train_df.isnull().sum()/train_df.isnull().count()*100\n",
"percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '% missing'])\n",
"missing_data.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total</th>\n",
" <th>% missing</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Cabin</th>\n",
" <td>687</td>\n",
" <td>77.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>177</td>\n",
" <td>19.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Embarked</th>\n",
" <td>2</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fare</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ticket</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total % missing\n",
"Cabin 687 77.1\n",
"Age 177 19.9\n",
"Embarked 2 0.2\n",
"Fare 0 0.0\n",
"Ticket 0 0.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LHm7kQY8w7Zs",
"colab_type": "text"
},
"source": [
"From above data, we can make following inferences:\n",
"\n",
"1. Around 77% of cabin data is missing. This % is huge. So, we can't afford to drop data row-wise. It's better to delete this column entirely since majority values are missing anyways.\n",
"\n",
"2. Embarked value can be easily filled.\n",
"\n",
"3. With some common sense, we can eliminate variables like PassengerId, Name and Ticket as we don't expect them to have much corelation with survival chance."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vr0zZm8mPZFu",
"colab_type": "code",
"outputId": "285cee9b-07a5-4ecc-92c6-6762e4afa491",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
}
},
"source": [
"survived = 'survived'\n",
"not_survived = 'not survived'\n",
"fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))\n",
"women = train_df[train_df['Sex']=='female']\n",
"men = train_df[train_df['Sex']=='male']\n",
"ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)\n",
"ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False)\n",
"ax.legend()\n",
"ax.set_title('Female')\n",
"ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False)\n",
"ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False)\n",
"ax.legend()\n",
"_ = ax.set_title('Male')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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ybb4ArgSWZebfd5m+Z5fZpgKPNb48qTa9+bhazdGO+8D+pf6iHf9+BpLS17+Wa/MdDcwC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\nYUqSJKmAYUqSJKmAYUqSJKnA/wdhpYOuQMTQigAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GFJX-UZD1jNe",
"colab_type": "code",
"outputId": "f2a875ed-b7ff-485c-b884-21b8a1977c4a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"sns.barplot(x='Pclass', y='Survived', data=train_df)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f98e6d519b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
},
{
"output_type": "display_data",
"data": {
"image/png": 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yO8kWgCQ/nuQk8Brg/UmO9JVHkrSwPn99RFUdAA7MGts1cP8QM5uVJEnLwIrY0SxJWhqW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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nKYfbR-N1vsd",
"colab_type": "code",
"outputId": "fc9457a6-25f2-437b-cd6e-b3f20a10bdda",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"#Combining SibSp and Parch to show if someone is alone or not\n",
"data = [train_df, test_df]\n",
"for dataset in data:\n",
" dataset['relatives'] = dataset['SibSp'] + dataset['Parch']\n",
" dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0\n",
" dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1\n",
" dataset['not_alone'] = dataset['not_alone'].astype(int)\n",
"train_df['not_alone'].value_counts()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1 537\n",
"0 354\n",
"Name: not_alone, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GiygRG_X2GXR",
"colab_type": "code",
"colab": {}
},
"source": [
"train_df = train_df.drop(['PassengerId','Cabin'], axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L_iMmf8W2wQp",
"colab_type": "code",
"outputId": "0c233087-5040-49f0-d7ca-eae26e787e88",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"data = [train_df, test_df]\n",
"\n",
"for dataset in data:\n",
" mean = train_df[\"Age\"].mean()\n",
" std = test_df[\"Age\"].std()\n",
" is_null = dataset[\"Age\"].isnull().sum()\n",
" # compute random numbers between the mean, std and is_null\n",
" rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n",
" # fill NaN values in Age column with random values generated\n",
" age_slice = dataset[\"Age\"].copy()\n",
" age_slice[np.isnan(age_slice)] = rand_age\n",
" dataset[\"Age\"] = age_slice\n",
" dataset[\"Age\"] = train_df[\"Age\"].astype(int)\n",
"train_df[\"Age\"].isnull().sum()\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9oiazbi23F3i",
"colab_type": "code",
"outputId": "e7ef4238-7ad0-4077-dba9-817377088d47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"source": [
"train_df['Embarked'].describe()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 889\n",
"unique 3\n",
"top S\n",
"freq 644\n",
"Name: Embarked, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "91r6YkKR3NJU",
"colab_type": "code",
"colab": {}
},
"source": [
"common_value = 'S'\n",
"data = [train_df, test_df]\n",
"\n",
"for dataset in data:\n",
" dataset['Embarked'] = dataset['Embarked'].fillna(common_value)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2feOofoi3P3K",
"colab_type": "code",
"outputId": "f3a91856-21c0-405c-8215-9ae05afa7057",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
}
},
"source": [
"train_df.info()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 891 non-null int64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Embarked 891 non-null object\n",
"relatives 891 non-null int64\n",
"not_alone 891 non-null int64\n",
"dtypes: float64(1), int64(7), object(4)\n",
"memory usage: 83.6+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aWEXlXZc3UQf",
"colab_type": "code",
"colab": {}
},
"source": [
"train_df = train_df.drop(['Ticket','Name'], axis=1)\n",
"test_df = test_df.drop(['Ticket','Name'], axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "QZhOp69G38gC",
"colab_type": "code",
"colab": {}
},
"source": [
"genders = {\"male\": 0, \"female\": 1}\n",
"data = [train_df, test_df]\n",
"\n",
"for dataset in data:\n",
" dataset['Sex'] = dataset['Sex'].map(genders)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "USyX_tSg4C9N",
"colab_type": "code",
"colab": {}
},
"source": [
"ports = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
"data = [train_df, test_df]\n",
"\n",
"for dataset in data:\n",
" dataset['Embarked'] = dataset['Embarked'].map(ports)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ApXleNFj4Df1",
"colab_type": "code",
"outputId": "4a146c54-e5e6-450e-f7e2-d87283fd13f4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"data = [train_df, test_df]\n",
"for dataset in data:\n",
" dataset['Age'] = dataset['Age'].astype(int)\n",
" dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n",
" dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
" dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
" dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
" dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
" dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
" dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
" dataset.loc[ dataset['Age'] > 66, 'Age'] = 6\n",
"\n",
"# let's see how it's distributed \n",
"train_df['Age'].value_counts()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4 170\n",
"6 154\n",
"5 146\n",
"3 134\n",
"2 122\n",
"1 97\n",
"0 68\n",
"Name: Age, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "H8sr8fSz4rbt",
"colab_type": "code",
"outputId": "9b42d801-183e-447b-d601-e47057aae644",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
}
},
"source": [
"train_df.head(10)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Embarked</th>\n",
" <th>relatives</th>\n",
" <th>not_alone</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age ... Fare Embarked relatives not_alone\n",
"0 0 3 0 2 ... 7 0 1 0\n",
"1 1 1 1 5 ... 71 1 1 0\n",
"2 1 3 1 3 ... 7 0 0 1\n",
"3 1 1 1 5 ... 53 0 1 0\n",
"4 0 3 0 5 ... 8 0 0 1\n",
"5 0 3 0 4 ... 8 2 0 1\n",
"6 0 1 0 6 ... 51 0 0 1\n",
"7 0 3 0 0 ... 21 0 4 0\n",
"8 1 3 1 3 ... 11 0 2 0\n",
"9 1 2 1 1 ... 30 1 1 0\n",
"\n",
"[10 rows x 10 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jSkVyUP34t77",
"colab_type": "code",
"colab": {}
},
"source": [
"X_train = train_df.drop([\"Survived\",\"Fare\"], axis=1)\n",
"Y_train = train_df[\"Survived\"]\n",
"X_test = test_df.drop([\"PassengerId\",\"Cabin\",\"Fare\"], axis=1).copy()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WzFHuTHj5u2g",
"colab_type": "code",
"outputId": "83b6c27d-db12-4f08-e5a8-5e4cd3eec328",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"X_train.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Embarked</th>\n",
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" <th>not_alone</th>\n",
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" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp Parch Embarked relatives not_alone\n",
"0 3 0 2 1 0 0 1 0\n",
"1 1 1 5 1 0 1 1 0\n",
"2 3 1 3 0 0 0 0 1\n",
"3 1 1 5 1 0 0 1 0\n",
"4 3 0 5 0 0 0 0 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9pOckEeq50PC",
"colab_type": "code",
"outputId": "ce603a97-d8dd-4f34-9ab1-4c5f98649ecc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"X_test.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Embarked</th>\n",
" <th>relatives</th>\n",
" <th>not_alone</th>\n",
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" <th>2</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp Parch Embarked relatives not_alone\n",
"0 3 0 2 0 0 2 0 1\n",
"1 3 1 5 1 0 0 1 0\n",
"2 2 0 3 0 0 2 0 1\n",
"3 3 0 5 0 0 0 0 1\n",
"4 3 1 5 1 1 0 2 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xfCIAPAh5Opm",
"colab_type": "code",
"outputId": "35504ebc-9a7c-4091-cbd2-63063b6a1fa2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"logreg = LogisticRegression()\n",
"logreg.fit(X_train, Y_train)\n",
"\n",
"Y_pred = logreg.predict(X_test)\n",
"\n",
"acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
"print(\"Accuracy via logistic regression: \",acc_log)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy via logistic regression: 80.58\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dEt0at6c6Vbc",
"colab_type": "code",
"outputId": "2e2f2dff-50dd-4723-9fca-f8b4f5c07f99",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"from sklearn.model_selection import cross_val_score\n",
"\n",
"scores = cross_val_score(logreg, X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
"print(\"Scores:\", scores)\n",
"print(\"Mean:\", scores.mean())\n",
"print(\"Standard Deviation:\", scores.std())"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Scores: [0.78888889 0.76666667 0.7752809 0.84269663 0.80898876 0.79775281\n",
" 0.79775281 0.7752809 0.82022472 0.80681818]\n",
"Mean: 0.7980351265463625\n",
"Standard Deviation: 0.021880254046102804\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dnKHfSxW7ZpK",
"colab_type": "text"
},
"source": [
"This shows us that the model accuracy using Logistic regression is 80.58% with an error of 2.18%."
]
}
]
}
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