From 30d47e0e2d7cfeb45d66482c81445f603574999b Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 29 Jul 2021 22:10:20 +0200 Subject: [PATCH 1/6] 29/07/2021 --- .gitignore.txt | 154 + Code/Titanic_Data_Cleaning_AN.ipynb | 4530 +++++++ Code/Titanic_Predictions_AN.ipynb | 10074 ++++++++++++++++ Code/Titanic_Visualizations_AN.ipynb | 793 ++ Data/test.csv | 419 + Data/train.csv | 892 ++ Data/train_cleaned_knn_imputation.csv | 892 ++ ...leaned_knn_imputation_without_outliers.csv | 826 ++ README.md | 81 + your-project/README.md | 72 - 10 files changed, 18661 insertions(+), 72 deletions(-) create mode 100644 .gitignore.txt create mode 100644 Code/Titanic_Data_Cleaning_AN.ipynb create mode 100644 Code/Titanic_Predictions_AN.ipynb create mode 100644 Code/Titanic_Visualizations_AN.ipynb create mode 100644 Data/test.csv create mode 100644 Data/train.csv create mode 100644 Data/train_cleaned_knn_imputation.csv create mode 100644 Data/train_cleaned_knn_imputation_without_outliers.csv create mode 100644 README.md delete mode 100644 your-project/README.md diff --git a/.gitignore.txt b/.gitignore.txt new file mode 100644 index 0000000..2631c35 --- /dev/null +++ b/.gitignore.txt @@ -0,0 +1,154 @@ + +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +pytestdebug.log + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +doc/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +# .env +.env/ +.venv/ +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pythonenv* + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# operating system-related files +# file properties cache/storage on macOS +*.DS_Store +# thumbnail cache on Windows +Thumbs.db + +# profiling data +.prof + + +# End of https://www.toptal.com/developers/gitignore/api/python \ No newline at end of file diff --git a/Code/Titanic_Data_Cleaning_AN.ipynb b/Code/Titanic_Data_Cleaning_AN.ipynb new file mode 100644 index 0000000..f2221d5 --- /dev/null +++ b/Code/Titanic_Data_Cleaning_AN.ipynb @@ -0,0 +1,4530 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cd5a8f4c", + "metadata": {}, + "source": [ + "**TITANIC - DATA CLEANING - IRONHACK**" + ] + }, + { + "cell_type": "markdown", + "id": "9af3bf88", + "metadata": {}, + "source": [ + "# Understanding the data." + ] + }, + { + "cell_type": "markdown", + "id": "bdedcb4c", + "metadata": {}, + "source": [ + "## Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aebd8a95", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.impute import KNNImputer\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "id": "c4de5798", + "metadata": {}, + "source": [ + "## Input data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9c8c2cee", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./data/train.csv')\n", + "titanic = df.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "0ede0684", + "metadata": {}, + "source": [ + "## Overview" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "207ac1a4", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", + "

891 rows × 12 columns

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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", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 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", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 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 \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic" + ] + }, + { + "cell_type": "markdown", + "id": "4c4b7017", + "metadata": {}, + "source": [ + "**What does each column stands for?** \n", + "- `PassengerId` -> Identification number for each person \n", + "- `Survived` -> Whether passenger survived or died ->\t0 = No, 1 = Yes \n", + "- `Pclass` -> Shows\tticket class ->\t1 = 1st, 2 = 2nd, 3 = 3rd\n", + "- `Name` -> Passenger's name \n", + "- `Sex` -> Passenger's gender -> male for men, and female for woman\t \n", + "- `Age`\t-> Passenger's age in years\t\n", + "- `SibSp` -> With how many siblings or spouses travel this passenger aboard the Titanic\t \t\n", + "- `Parch` -> With how many parents or children travel this passenger aboard the Titanic\t \n", + "- `Ticket` ->\tTicket number\t\n", + "- `Fare` -> \tTicket's price\t\n", + "- `Cabin` -> Cabin number\t\n", + "- `Embarked` ->\tPort of Embarkation\t-> C = Cherbourg, Q = Queenstown, S = Southampton\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae2e5137", + "metadata": {}, + "source": [ + "## Number of columns and rows?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e8dbf644", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(891, 12)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.shape" + ] + }, + { + "cell_type": "markdown", + "id": "e1c160df", + "metadata": {}, + "source": [ + "There are 12 columns and 891 rows (passengers)" + ] + }, + { + "cell_type": "markdown", + "id": "bf98bcb0", + "metadata": {}, + "source": [ + "## Type of variables (numerical (discrete, continual), categorical(nominal, ordinal), alphanumerical)?" + ] + }, + { + "cell_type": "markdown", + "id": "f7f8f0b8", + "metadata": {}, + "source": [ + "Numerical variables: \n", + "- `Age` (Ordinal - Discrete) \n", + "- `SibSp` (Ordinal - Discrete) \n", + "- `Parch` (Ordinal - Discrete) \n", + "- `Fare` (Ratio - Continual) \n", + "\n", + "Categorical variables: \n", + "- `PassengerId` (Nominal)\n", + "- `Survived` (Nominal - Binary)\n", + "- `Pclass` (Ordinal) \n", + "- `Name` (Nominal) \n", + "- `Sex` (Nominal - Binary) \n", + "- `Ticket` (Ordinal - Alphanumerical)\n", + "- `Cabin` (Nominal - Alphanumerical)\n", + "- `Embarked` (Nominal)\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "66754512", + "metadata": {}, + "source": [ + "## How they are encoded?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a74a34b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Survived int64\n", + "Pclass int64\n", + "Name object\n", + "Sex object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "bba6456d", + "metadata": {}, + "source": [ + "Although we can improve some variables, everything looks okay. Depending on the analysis we're going to perform it's possible to change data type at a later stage. For now we will leave it the way they are. " + ] + }, + { + "cell_type": "markdown", + "id": "57ec1940", + "metadata": {}, + "source": [ + "## Statistical summary." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9e5d2383", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "9743ac01", + "metadata": {}, + "source": [ + "What do we know from this table? \n", + "\n", + "- `PassengerId` tells us that there are 891 passengers on the board. The other metrics aren't valuable because that is categorical variable. \n", + "- `Survived` doesn't tells us something valuable, apart from fact that we know either the passenger survived `1` or died `0`, because is categorical variable. \n", + "- `Pclass` again doesn't tells us something valuable, apart from fact that there are three type of passenger's class from `1`, `2` and `3`. \n", + "- `Age` tells us that there are information for age of 714 passengers, that means that ages of 177 passengers are missing. Initially we know age is fractional if less than 1 year. So the youngest person is baby and the oldest passengers is 80 years old. Also 50% of these 714 people on board are younger or older than 28 years. \n", + "- `SibSp` tells us that there are passengers, which travel without siblings or spouses and also the highest number of siblings or spouses is 8. Half of the people on board travel alone (without siblings or spouses) and the other half with at least one siblings or spouses. \n", + "- `Parch` tells us that there are passengers, which travel without parents or children and also the highest number of parents or children is 6. Half of the people on board travel without parents or children and the other half with at least with one child or parent. \n", + "- `Fare` tells us that the cheapest ticket is free (or there is typo), whereas the most expensive cost 512,33 dollars (or possible outlier). \n", + " \n", + "Although this is a numerical overview of our data, let's keep in mind that our data still is not cleaned and there are missing values. So it's to early for conclusions. " + ] + }, + { + "cell_type": "markdown", + "id": "4fef0519", + "metadata": {}, + "source": [ + "# Cleaning dataset for visualization" + ] + }, + { + "cell_type": "markdown", + "id": "62e66b9e", + "metadata": {}, + "source": [ + "## Dropping duplicates?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8590353c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "35c687a4", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.duplicated('PassengerId').sum()" + ] + }, + { + "cell_type": "markdown", + "id": "51c536ab", + "metadata": {}, + "source": [ + "There are not duplicated values." + ] + }, + { + "cell_type": "markdown", + "id": "5a804ea8", + "metadata": {}, + "source": [ + "## Missing values?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "545afb77", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "Survived 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 177\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 0\n", + "Cabin 687\n", + "Embarked 2\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "a7603b82", + "metadata": {}, + "source": [ + "There are 177 missing values in `Age` and 687 in `Cabin`" + ] + }, + { + "cell_type": "markdown", + "id": "c67764a7", + "metadata": {}, + "source": [ + "### Let's see the ratio of missing values in `Cabin`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "37ad8eee", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in 'Cabin' are: 77.10%\n" + ] + } + ], + "source": [ + "missing_values_in_cabin = titanic['Cabin'].isna().sum()\n", + "total_values_in_cabin = len(titanic['Cabin'])\n", + "\n", + "ratio_missing_values_in_cabin = ( missing_values_in_cabin / total_values_in_cabin ) * 100\n", + "\n", + "print(f\"Missing values in 'Cabin' are: {ratio_missing_values_in_cabin:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ce3f62b", + "metadata": {}, + "source": [ + "There are so many missing values in `Cabin`, let's drop this column and save the result in other variable, because we will need uncleaned titanic in future analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bb044440", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Cabin 204 non-null object \n", + " 11 Embarked 889 non-null object \n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 83.7+ KB\n" + ] + } + ], + "source": [ + "titanic.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3385ab3", + "metadata": {}, + "outputs": [], + "source": [ + "titanic_new = titanic.drop('Cabin', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "40020a7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Embarked 889 non-null object \n", + "dtypes: float64(2), int64(5), object(4)\n", + "memory usage: 76.7+ KB\n" + ] + } + ], + "source": [ + "titanic_new.info()" + ] + }, + { + "cell_type": "markdown", + "id": "e4c017d3", + "metadata": {}, + "source": [ + "### Let's see the ratio of missing values in `Age`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "93a19402", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in 'Age' are: 19.87%\n" + ] + } + ], + "source": [ + "missing_values_in_age = titanic['Age'].isnull().sum()\n", + "total_values_in_age = len(titanic['Age'])\n", + "\n", + "ratio_missing_values_in_age = ( missing_values_in_age / total_values_in_age ) * 100\n", + "\n", + "print(f\"Missing values in 'Age' are: {ratio_missing_values_in_age:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "74ff42b8", + "metadata": {}, + "source": [ + "Here we have less missing values, so we will try to restore them" + ] + }, + { + "cell_type": "markdown", + "id": "d52041d0", + "metadata": {}, + "source": [ + "### Restoring missing values in `Age` with KNN Imputation" + ] + }, + { + "cell_type": "markdown", + "id": "d24c22f3", + "metadata": {}, + "source": [ + "#### Preparation of data for KNN Imputation " + ] + }, + { + "cell_type": "markdown", + "id": "7d5fe7fd", + "metadata": {}, + "source": [ + "First, we will remove some of the variables that have little or no effect for KNN Imputer. \n", + "\n", + "Columns to be removed: \n", + "`PassengerId`, `Name`, `Ticket`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1dfcb445", + "metadata": {}, + "outputs": [], + "source": [ + "knn_imputation_columns_to_be_removed = ['PassengerId', 'Name', 'Ticket']\n", + "\n", + "knn_df = titanic_new.drop(knn_imputation_columns_to_be_removed, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "8f903f6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age 177\n", + "Embarked 2\n", + "Survived 0\n", + "Pclass 0\n", + "Sex 0\n", + "SibSp 0\n", + "Parch 0\n", + "Fare 0\n", + "dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df.isna().sum().sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "089146c6", + "metadata": {}, + "source": [ + "As we know initially `KNN Imputer` does not recognize text data values, therefore we will need to dumify our text variables." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "225093e1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Survived int64\n", + "Pclass int64\n", + "Sex object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Fare float64\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "79e48111", + "metadata": {}, + "source": [ + "Our categorical variables are `Sex` and `Embarked`. Let's dumify them" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a62786ae", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Sex_male Embarked_Q Embarked_S\n", + "0 1 0 1\n", + "1 0 0 0\n", + "2 0 0 1\n", + "3 0 0 1\n", + "4 1 0 1\n", + ".. ... ... ...\n", + "886 1 0 1\n", + "887 0 0 1\n", + "888 0 0 1\n", + "889 1 0 0\n", + "890 1 1 0\n", + "\n", + "[891 rows x 3 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get categorical variables\n", + "cat_variables = knn_df[['Sex', 'Embarked']]\n", + "\n", + "# dumify categorical variables\n", + "cat_dummies = pd.get_dummies(cat_variables, drop_first=True)\n", + "cat_dummies" + ] + }, + { + "cell_type": "markdown", + "id": "3dc7485c", + "metadata": {}, + "source": [ + "From table above we can see that: \n", + "`Sex_male` is = 1 when passenger is male and = 0 when is woman. \n", + "`Embarked_Q` is = 1 when passenger has boarder from Queenstown. \n", + "`Embarked_S` is = 1 when passenger has boarder from Southampton. \n", + "If `Embarked_Q` and `Embarked_S` = 0 this means, that passenger has boarder from Cherbourg. " + ] + }, + { + "cell_type": "markdown", + "id": "e0f52fd1", + "metadata": {}, + "source": [ + "Let's put our dumify variables in knn dataframe. \n", + "First drop the old ones and then put the dumify." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d9e8fb2b", + "metadata": {}, + "outputs": [], + "source": [ + "knn_df.drop(cat_variables, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7d51e542", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male Embarked_Q \\\n", + "0 0 3 22.0 1 0 7.2500 1 0 \n", + "1 1 1 38.0 1 0 71.2833 0 0 \n", + "2 1 3 26.0 0 0 7.9250 0 0 \n", + "3 1 1 35.0 1 0 53.1000 0 0 \n", + "4 0 3 35.0 0 0 8.0500 1 0 \n", + "\n", + " Embarked_S \n", + "0 1 \n", + "1 0 \n", + "2 1 \n", + "3 1 \n", + "4 1 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ad8ec9b4", + "metadata": {}, + "source": [ + "Now our data is almost ready for KNN Imputation." + ] + }, + { + "cell_type": "markdown", + "id": "6e6cca2c", + "metadata": {}, + "source": [ + "As we know KNN Imputer is a distance-based imputation method and it requires us to normalize our data. Otherwise, the different scales of our data will lead the KNN Imputer to generate biased replacements for the missing values. For simplicity, we will use Scikit-Learn's MinMaxScaler which will scale our variables to have values between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "3bce82d4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male \\\n", + "0 0.0 1.0 0.271174 0.125 0.000000 0.014151 1.0 \n", + "1 1.0 0.0 0.472229 0.125 0.000000 0.139136 0.0 \n", + "2 1.0 1.0 0.321438 0.000 0.000000 0.015469 0.0 \n", + "3 1.0 0.0 0.434531 0.125 0.000000 0.103644 0.0 \n", + "4 0.0 1.0 0.434531 0.000 0.000000 0.015713 1.0 \n", + ".. ... ... ... ... ... ... ... \n", + "886 0.0 0.5 0.334004 0.000 0.000000 0.025374 1.0 \n", + "887 1.0 0.0 0.233476 0.000 0.000000 0.058556 0.0 \n", + "888 0.0 1.0 NaN 0.125 0.333333 0.045771 0.0 \n", + "889 1.0 0.0 0.321438 0.000 0.000000 0.058556 1.0 \n", + "890 0.0 1.0 0.396833 0.000 0.000000 0.015127 1.0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0.0 1.0 \n", + "1 0.0 0.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + ".. ... ... \n", + "886 0.0 1.0 \n", + "887 0.0 1.0 \n", + "888 0.0 1.0 \n", + "889 0.0 0.0 \n", + "890 1.0 0.0 \n", + "\n", + "[891 rows x 9 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# defining scaler\n", + "\n", + "scaler = MinMaxScaler()\n", + "\n", + "# fit the scaler\n", + "\n", + "scaler.fit(knn_df)\n", + "\n", + "# scale the data\n", + "\n", + "scaled_data = scaler.transform(knn_df)\n", + "\n", + "# generate df with scaled values\n", + "\n", + "knn_df = pd.DataFrame(scaled_data, columns=knn_df.columns)\n", + "\n", + "knn_df" + ] + }, + { + "cell_type": "markdown", + "id": "a2be306a", + "metadata": {}, + "source": [ + "Now our dataset has dummy variables and is normalized, we can proceed with KNN Imputation." + ] + }, + { + "cell_type": "markdown", + "id": "8d451646", + "metadata": {}, + "source": [ + "#### Building KNN Imputer" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c517d0e6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Survived 0\n", + "Pclass 0\n", + "Age 177\n", + "SibSp 0\n", + "Parch 0\n", + "Fare 0\n", + "Sex_male 0\n", + "Embarked_Q 0\n", + "Embarked_S 0\n", + "dtype: int64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "dd4cfbd7", + "metadata": {}, + "source": [ + "Clearly we can see that there are 177 missing values in `Age`. \n", + "Now we are going to replace missing values using KNN Imputer model, which will predict their values based on all other variables." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "94b0ae87", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male \\\n", + "0 0.0 1.0 0.271174 0.125 0.000000 0.014151 1.0 \n", + "1 1.0 0.0 0.472229 0.125 0.000000 0.139136 0.0 \n", + "2 1.0 1.0 0.321438 0.000 0.000000 0.015469 0.0 \n", + "3 1.0 0.0 0.434531 0.125 0.000000 0.103644 0.0 \n", + "4 0.0 1.0 0.434531 0.000 0.000000 0.015713 1.0 \n", + ".. ... ... ... ... ... ... ... \n", + "886 0.0 0.5 0.334004 0.000 0.000000 0.025374 1.0 \n", + "887 1.0 0.0 0.233476 0.000 0.000000 0.058556 0.0 \n", + "888 0.0 1.0 0.273687 0.125 0.333333 0.045771 0.0 \n", + "889 1.0 0.0 0.321438 0.000 0.000000 0.058556 1.0 \n", + "890 0.0 1.0 0.396833 0.000 0.000000 0.015127 1.0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0.0 1.0 \n", + "1 0.0 0.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + ".. ... ... \n", + "886 0.0 1.0 \n", + "887 0.0 1.0 \n", + "888 0.0 1.0 \n", + "889 0.0 0.0 \n", + "890 1.0 0.0 \n", + "\n", + "[891 rows x 9 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# defining imputer with 5 neigbours measured by Euclidean distance.\n", + "\n", + "imputer = KNNImputer(n_neighbors=5)\n", + "\n", + "# fit the imputer\n", + "\n", + "imputer.fit(knn_df)\n", + "\n", + "# generate predicted ages\n", + "\n", + "knn_ages = imputer.transform(knn_df)\n", + "\n", + "# replace missing values with generated ages\n", + "\n", + "knn_df = pd.DataFrame(knn_ages, columns=knn_df.columns)\n", + "\n", + "knn_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0cae8a58", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Survived 0\n", + "Pclass 0\n", + "Age 0\n", + "SibSp 0\n", + "Parch 0\n", + "Fare 0\n", + "Sex_male 0\n", + "Embarked_Q 0\n", + "Embarked_S 0\n", + "dtype: int64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "e899868f", + "metadata": {}, + "source": [ + "Our dataset has no longer missing values, they have been imputed as the means of k-Nearest Neighbor values." + ] + }, + { + "cell_type": "markdown", + "id": "a28a4af9", + "metadata": {}, + "source": [ + "#### Converting scaled values to real values" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "5f684369", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male Embarked_Q \\\n", + "0 0.0 3.0 22.0 1.0 0.0 7.2500 1.0 0.0 \n", + "1 1.0 1.0 38.0 1.0 0.0 71.2833 0.0 0.0 \n", + "2 1.0 3.0 26.0 0.0 0.0 7.9250 0.0 0.0 \n", + "3 1.0 1.0 35.0 1.0 0.0 53.1000 0.0 0.0 \n", + "4 0.0 3.0 35.0 0.0 0.0 8.0500 1.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "886 0.0 2.0 27.0 0.0 0.0 13.0000 1.0 0.0 \n", + "887 1.0 1.0 19.0 0.0 0.0 30.0000 0.0 0.0 \n", + "888 0.0 3.0 22.2 1.0 2.0 23.4500 0.0 0.0 \n", + "889 1.0 1.0 26.0 0.0 0.0 30.0000 1.0 0.0 \n", + "890 0.0 3.0 32.0 0.0 0.0 7.7500 1.0 1.0 \n", + "\n", + " Embarked_S \n", + "0 1.0 \n", + "1 0.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + ".. ... \n", + "886 1.0 \n", + "887 1.0 \n", + "888 1.0 \n", + "889 0.0 \n", + "890 0.0 \n", + "\n", + "[891 rows x 9 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df_real_values = pd.DataFrame(scaler.inverse_transform(knn_df), columns=knn_df.columns)\n", + "knn_df_real_values" + ] + }, + { + "cell_type": "markdown", + "id": "b046a726", + "metadata": {}, + "source": [ + "Now we have predicted age values based on knn method." + ] + }, + { + "cell_type": "markdown", + "id": "fe3f19eb", + "metadata": {}, + "source": [ + "#### Let's inspect our `Age` values" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "296d2fb1", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "24.00 30\n", + "22.00 27\n", + "18.00 26\n", + "30.00 26\n", + "28.00 25\n", + " ..\n", + "23.50 1\n", + "0.67 1\n", + "55.50 1\n", + "37.60 1\n", + "25.40 1\n", + "Name: Age, Length: 150, dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df_real_values['Age'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "20ab74d0", + "metadata": {}, + "source": [ + "We can see that there are some values that doesn't make sens, like `23.50`, `37.60`, the ages can't be decimal numbers (exception are children under 1 year), we are either 37 or 38. Therefore we want to round this numbers. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "459a4c50", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "knn_df_real_values['Age'] = knn_df_real_values['Age'].apply(lambda x: np.around(x) if x > 1 else x)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "bf40c1a9", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "24.00 45\n", + "18.00 45\n", + "28.00 34\n", + "30.00 34\n", + "22.00 32\n", + " ..\n", + "80.00 1\n", + "66.00 1\n", + "53.00 1\n", + "74.00 1\n", + "0.42 1\n", + "Name: Age, Length: 75, dtype: int64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df_real_values['Age'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "7e1db540", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male Embarked_Q \\\n", + "0 0.0 3.0 22.0 1.0 0.0 7.2500 1.0 0.0 \n", + "1 1.0 1.0 38.0 1.0 0.0 71.2833 0.0 0.0 \n", + "2 1.0 3.0 26.0 0.0 0.0 7.9250 0.0 0.0 \n", + "3 1.0 1.0 35.0 1.0 0.0 53.1000 0.0 0.0 \n", + "4 0.0 3.0 35.0 0.0 0.0 8.0500 1.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "886 0.0 2.0 27.0 0.0 0.0 13.0000 1.0 0.0 \n", + "887 1.0 1.0 19.0 0.0 0.0 30.0000 0.0 0.0 \n", + "888 0.0 3.0 22.0 1.0 2.0 23.4500 0.0 0.0 \n", + "889 1.0 1.0 26.0 0.0 0.0 30.0000 1.0 0.0 \n", + "890 0.0 3.0 32.0 0.0 0.0 7.7500 1.0 1.0 \n", + "\n", + " Embarked_S \n", + "0 1.0 \n", + "1 0.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + ".. ... \n", + "886 1.0 \n", + "887 1.0 \n", + "888 1.0 \n", + "889 0.0 \n", + "890 0.0 \n", + "\n", + "[891 rows x 9 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df_real_values" + ] + }, + { + "cell_type": "markdown", + "id": "936962e5", + "metadata": {}, + "source": [ + "Now everything is fine, we can proceed with updating our initial dataframe" + ] + }, + { + "cell_type": "markdown", + "id": "fadbf3fb", + "metadata": {}, + "source": [ + "### Let's update our initial dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "4dc51832", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S
4503Allen, Mr. William Henrymale35.0003734508.0500S
....................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000S
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500S
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C
89089103Dooley, Mr. Patrickmale32.0003703767.7500Q
\n", + "

891 rows × 11 columns

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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", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 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", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 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 \n", + ".. ... ... ... ... \n", + "886 0 211536 13.0000 S \n", + "887 0 112053 30.0000 S \n", + "888 2 W./C. 6607 23.4500 S \n", + "889 0 111369 30.0000 C \n", + "890 0 370376 7.7500 Q \n", + "\n", + "[891 rows x 11 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_new" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "f8cb3933", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SurvivedPclassAgeSibSpParchFareSex_maleEmbarked_QEmbarked_S
00.03.022.01.00.07.25001.00.01.0
11.01.038.01.00.071.28330.00.00.0
21.03.026.00.00.07.92500.00.01.0
31.01.035.01.00.053.10000.00.01.0
40.03.035.00.00.08.05001.00.01.0
..............................
8860.02.027.00.00.013.00001.00.01.0
8871.01.019.00.00.030.00000.00.01.0
8880.03.022.01.02.023.45000.00.01.0
8891.01.026.00.00.030.00001.00.00.0
8900.03.032.00.00.07.75001.01.00.0
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891 rows × 9 columns

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" + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare Sex_male Embarked_Q \\\n", + "0 0.0 3.0 22.0 1.0 0.0 7.2500 1.0 0.0 \n", + "1 1.0 1.0 38.0 1.0 0.0 71.2833 0.0 0.0 \n", + "2 1.0 3.0 26.0 0.0 0.0 7.9250 0.0 0.0 \n", + "3 1.0 1.0 35.0 1.0 0.0 53.1000 0.0 0.0 \n", + "4 0.0 3.0 35.0 0.0 0.0 8.0500 1.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "886 0.0 2.0 27.0 0.0 0.0 13.0000 1.0 0.0 \n", + "887 1.0 1.0 19.0 0.0 0.0 30.0000 0.0 0.0 \n", + "888 0.0 3.0 22.0 1.0 2.0 23.4500 0.0 0.0 \n", + "889 1.0 1.0 26.0 0.0 0.0 30.0000 1.0 0.0 \n", + "890 0.0 3.0 32.0 0.0 0.0 7.7500 1.0 1.0 \n", + "\n", + " Embarked_S \n", + "0 1.0 \n", + "1 0.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + ".. ... \n", + "886 1.0 \n", + "887 1.0 \n", + "888 1.0 \n", + "889 0.0 \n", + "890 0.0 \n", + "\n", + "[891 rows x 9 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_df_real_values" + ] + }, + { + "cell_type": "markdown", + "id": "1d53f645", + "metadata": {}, + "source": [ + "### Updating `Age` and `Sex`" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "e3110441", + "metadata": {}, + "outputs": [], + "source": [ + "# Columns to be updated\n", + "\n", + "columns_to_be_updated = ['Age', 'Sex']\n", + "\n", + "# updating columns\n", + "titanic_new[columns_to_be_updated] = knn_df_real_values[['Age', 'Sex_male']]\n", + "\n", + "# renaming \"Sex\" to \"Sex_male\"\n", + "\n", + "titanic_new = titanic_new.rename(columns={'Sex': 'Sex_male'})" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "b18ec0a2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSex_maleAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harris1.022.010A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...0.038.010PC 1759971.2833C
2313Heikkinen, Miss. Laina0.026.000STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)0.035.01011380353.1000S
4503Allen, Mr. William Henry1.035.0003734508.0500S
....................................
88688702Montvila, Rev. Juozas1.027.00021153613.0000S
88788811Graham, Miss. Margaret Edith0.019.00011205330.0000S
88888903Johnston, Miss. Catherine Helen \"Carrie\"0.022.012W./C. 660723.4500S
88989011Behr, Mr. Karl Howell1.026.00011136930.0000C
89089103Dooley, Mr. Patrick1.032.0003703767.7500Q
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891 rows × 11 columns

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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", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex_male Age SibSp \\\n", + "0 Braund, Mr. Owen Harris 1.0 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0.0 38.0 1 \n", + "2 Heikkinen, Miss. Laina 0.0 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0.0 35.0 1 \n", + "4 Allen, Mr. William Henry 1.0 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas 1.0 27.0 0 \n", + "887 Graham, Miss. Margaret Edith 0.0 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 0.0 22.0 1 \n", + "889 Behr, Mr. Karl Howell 1.0 26.0 0 \n", + "890 Dooley, Mr. Patrick 1.0 32.0 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 \n", + ".. ... ... ... ... \n", + "886 0 211536 13.0000 S \n", + "887 0 112053 30.0000 S \n", + "888 2 W./C. 6607 23.4500 S \n", + "889 0 111369 30.0000 C \n", + "890 0 370376 7.7500 Q \n", + "\n", + "[891 rows x 11 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_new" + ] + }, + { + "cell_type": "markdown", + "id": "534e78c1", + "metadata": {}, + "source": [ + "### Updating `Embarked`" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ba8d22c1", + "metadata": {}, + "outputs": [], + "source": [ + "# adding two new columns, which represent one hot encoded \"Embarked\" values\n", + "\n", + "# get one-hot encoded \"Embarked\"\n", + "new_embarked = knn_df_real_values[['Embarked_Q', 'Embarked_S']]\n", + "\n", + "# concat OH encoded \"Embarked\" with titanic df\n", + "titanic_new = pd.concat([titanic_new, new_embarked], axis=1)\n", + "\n", + "# drop old \"Embarked\"\n", + "\n", + "titanic_new.drop('Embarked', axis=1, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "3ce2b666", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSex_maleAgeSibSpParchTicketFareEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harris1.022.010A/5 211717.25000.01.0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...0.038.010PC 1759971.28330.00.0
2313Heikkinen, Miss. Laina0.026.000STON/O2. 31012827.92500.01.0
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)0.035.01011380353.10000.01.0
4503Allen, Mr. William Henry1.035.0003734508.05000.01.0
.......................................
88688702Montvila, Rev. Juozas1.027.00021153613.00000.01.0
88788811Graham, Miss. Margaret Edith0.019.00011205330.00000.01.0
88888903Johnston, Miss. Catherine Helen \"Carrie\"0.022.012W./C. 660723.45000.01.0
88989011Behr, Mr. Karl Howell1.026.00011136930.00000.00.0
89089103Dooley, Mr. Patrick1.032.0003703767.75001.00.0
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891 rows × 12 columns

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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", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex_male Age SibSp \\\n", + "0 Braund, Mr. Owen Harris 1.0 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0.0 38.0 1 \n", + "2 Heikkinen, Miss. Laina 0.0 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0.0 35.0 1 \n", + "4 Allen, Mr. William Henry 1.0 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas 1.0 27.0 0 \n", + "887 Graham, Miss. Margaret Edith 0.0 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 0.0 22.0 1 \n", + "889 Behr, Mr. Karl Howell 1.0 26.0 0 \n", + "890 Dooley, Mr. Patrick 1.0 32.0 0 \n", + "\n", + " Parch Ticket Fare Embarked_Q Embarked_S \n", + "0 0 A/5 21171 7.2500 0.0 1.0 \n", + "1 0 PC 17599 71.2833 0.0 0.0 \n", + "2 0 STON/O2. 3101282 7.9250 0.0 1.0 \n", + "3 0 113803 53.1000 0.0 1.0 \n", + "4 0 373450 8.0500 0.0 1.0 \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 0.0 1.0 \n", + "887 0 112053 30.0000 0.0 1.0 \n", + "888 2 W./C. 6607 23.4500 0.0 1.0 \n", + "889 0 111369 30.0000 0.0 0.0 \n", + "890 0 370376 7.7500 1.0 0.0 \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_new" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "1474862b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex_male 891 non-null float64\n", + " 5 Age 891 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Embarked_Q 891 non-null float64\n", + " 11 Embarked_S 891 non-null float64\n", + "dtypes: float64(5), int64(5), object(2)\n", + "memory usage: 83.7+ KB\n" + ] + } + ], + "source": [ + "titanic_new.info()" + ] + }, + { + "cell_type": "markdown", + "id": "b7fc9467", + "metadata": {}, + "source": [ + "The dataset is generally cleaned, the missing values are restored, the columns with huge number of missing values are dropped, the categorical values are transformed to numerical and so on. Of course we can continue to clean and process more columns, but it all depends on what model we will use. For now, this is enough for visualizing the data. " + ] + }, + { + "cell_type": "markdown", + "id": "6981ab15", + "metadata": {}, + "source": [ + "**Data cleaning performed so far is for visualization and is extracted in 4.1**" + ] + }, + { + "cell_type": "markdown", + "id": "6d9c2d3b", + "metadata": {}, + "source": [ + "# Data cleaning for predictions" + ] + }, + { + "cell_type": "markdown", + "id": "537dce37", + "metadata": {}, + "source": [ + "## Identify outliers with boxplot approach " + ] + }, + { + "cell_type": "markdown", + "id": "f0f21725", + "metadata": {}, + "source": [ + "A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed. Outliers appears above or below the minimum and maximum of the boxplot." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "312021c6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new['Age'])\n", + "ax.set_title('Boxplot by Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "a3367e27", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new['SibSp'])\n", + "ax.set_title('Boxplot by Siblings and Spouses')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "77c43e2c", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new['Parch'])\n", + "ax.set_title('Boxplot by Parents and Children')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "50032696", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new['Fare'])\n", + "ax.set_title('Boxplot by Fare')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "f456e6c6", + "metadata": {}, + "outputs": [], + "source": [ + "#ax = sns.boxplot(y='Fare', x='Pclass', data= titanic_new)\n", + "#ax.set_title('Boxplot by Fare and Pclass')\n", + "#plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "0d5c47b6", + "metadata": {}, + "outputs": [], + "source": [ + "#titanic_new[titanic_new['Pclass'] == 1][['Fare','SibSp','Survived']].sort_values(by='Fare',ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "5a3b3fcb", + "metadata": {}, + "source": [ + "After we identify our outliers, let's drop them based on standard deviation approach and the again compare with boxplot in order to check whether all outliers are dropped." + ] + }, + { + "cell_type": "markdown", + "id": "91b6437d", + "metadata": {}, + "source": [ + "## Dropping outliers based on standard deviation" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "908ee1ee", + "metadata": {}, + "outputs": [], + "source": [ + "# this function return a list of index values for the outliers \n", + "def get_outliers(data, columns):\n", + " # we create an empty list\n", + " outlier_idxs = []\n", + " for col in columns:\n", + " elements = data[col]\n", + " # we get the mean value for each column\n", + " mean = elements.mean()\n", + " # and the standard deviation of the column\n", + " sd = elements.std()\n", + " # we then get the index values of all values higher or lower than the mean +/- 2 standard deviations\n", + " outliers_mask = data[(data[col] > mean + 3*sd) | (data[col] < mean - 3*sd)].index\n", + " # and add those values to our list\n", + " outlier_idxs += [x for x in outliers_mask]\n", + " return list(set(outlier_idxs))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3fad862d", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# here we define the columns where we have identified there could be outliers\n", + "columns_with_outliers = ['Age', 'SibSp', 'Parch', 'Fare']\n", + "\n", + "# we call the function we just created on the cookies dataset\n", + "titanic_new_outliers_indexes = get_outliers(titanic_new, columns_with_outliers)\n", + "\n", + "# and drop those values from our feature and target values\n", + "titanic_new_without_outliers = titanic_new.drop(titanic_new_outliers_indexes, axis = 0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "d4b9053b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSex_maleAgeSibSpParchTicketFareEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harris1.022.010A/5 211717.25000.01.0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...0.038.010PC 1759971.28330.00.0
2313Heikkinen, Miss. Laina0.026.000STON/O2. 31012827.92500.01.0
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)0.035.01011380353.10000.01.0
4503Allen, Mr. William Henry1.035.0003734508.05000.01.0
.......................................
88688702Montvila, Rev. Juozas1.027.00021153613.00000.01.0
88788811Graham, Miss. Margaret Edith0.019.00011205330.00000.01.0
88888903Johnston, Miss. Catherine Helen \"Carrie\"0.022.012W./C. 660723.45000.01.0
88989011Behr, Mr. Karl Howell1.026.00011136930.00000.00.0
89089103Dooley, Mr. Patrick1.032.0003703767.75001.00.0
\n", + "

825 rows × 12 columns

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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", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex_male Age SibSp \\\n", + "0 Braund, Mr. Owen Harris 1.0 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0.0 38.0 1 \n", + "2 Heikkinen, Miss. Laina 0.0 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0.0 35.0 1 \n", + "4 Allen, Mr. William Henry 1.0 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas 1.0 27.0 0 \n", + "887 Graham, Miss. Margaret Edith 0.0 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 0.0 22.0 1 \n", + "889 Behr, Mr. Karl Howell 1.0 26.0 0 \n", + "890 Dooley, Mr. Patrick 1.0 32.0 0 \n", + "\n", + " Parch Ticket Fare Embarked_Q Embarked_S \n", + "0 0 A/5 21171 7.2500 0.0 1.0 \n", + "1 0 PC 17599 71.2833 0.0 0.0 \n", + "2 0 STON/O2. 3101282 7.9250 0.0 1.0 \n", + "3 0 113803 53.1000 0.0 1.0 \n", + "4 0 373450 8.0500 0.0 1.0 \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 0.0 1.0 \n", + "887 0 112053 30.0000 0.0 1.0 \n", + "888 2 W./C. 6607 23.4500 0.0 1.0 \n", + "889 0 111369 30.0000 0.0 0.0 \n", + "890 0 370376 7.7500 1.0 0.0 \n", + "\n", + "[825 rows x 12 columns]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_new_without_outliers" + ] + }, + { + "cell_type": "markdown", + "id": "608d0a47", + "metadata": {}, + "source": [ + "Outliers are dropped. Now we have 825 passengers, before dropping outliers we had 891 passengers." + ] + }, + { + "cell_type": "markdown", + "id": "adbd6753", + "metadata": {}, + "source": [ + "## Let's compare again with boxplot approach whether outliers are dropped." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "fd4db711", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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XhzTpXPhb4M+AffX6LJpbawH+PSJ6I+Kt9bYmngPPAnYAn6mndv4hImbQzFpbvR64rr49IbX6JtwhlOpXX6M+oxcRHcCXgPeUUh5v3dekekspe0v1ku404PnAObkVDS0iLgEeKaX0ZtdymC4spTyPalrvnRHxktadDToH2oHnAZ8opZwPPMGgl/ANqhWAep7/UuD6wfvGs9bxDODtwOkt66fV25rs4Yg4FaD++UhyPftFxNOowvfzpZQv15sbWy9AKeUx4BtUL+NPjIj2eldTzoUXA5dGxDbgC1TTEFfRzFoppWyvfz5CNU/5fJp5DjwAPFBKub1eX08VyE2sdcArge+WUh6u1yek1vEM4O8AZ9XvKB9DNby/YRz7Gws3AMvq28uo5lrTRUQA/whsLaVc2bKrcfVGxMkRcWJ9ezrVXPVWqiBeXB/WiFpLKZeVUk4rpXRSnZ+bSylvoIG1RsSMiDh+4DbVfOXdNPAcKKU8BNwfEWfXm14O/JAG1tpiCf8//QATVes4T2pfDPyIag7wA9mT7INquw54EPgV1W/st1DN/30d+DFwMzAzu8661gupXgJ9H9hSLxc3sV7gOcD36lrvBj5Ybz8TuAPoo3qZ9/TsWgfV/TLgxqbWWtd0Z738YOD/UxPPgbqu84Ce+jz4F+CkBtc6A9gJnNCybUJq9avIkpTEN+EkKYkBLElJDGBJSmIAS1ISA1iSkhjAmhQi4jURUSKikd+qk46EAazJYglwW/1TmhIMYDVefQ2MC6m+LPP6etu0iPh4fb3ZTRGxISIW1/vmRcSt9UVrvjbwlVKpaQxgTQavprq27I+AnRExD/h9qsuJzgX+iOp6EwPXzOgGFpdS5gGfBj6cUbQ0kvaRD5HSLaG6SA5UF81ZQnXuXl9K2Qc8FBHfqPefDTwb2FRdQoM2qq+cS41jAKvRImIm1VXKzo2IQhWohepqYEPeBfhBKeWCCSpROmJOQajpFgOfLaWcUUrpLKWcTvXXFnYBr6vngk+hupgOVH/J4OSI2D8lERG/nVG4NBIDWE23hINHu18CnkF1FbsfAp+j+tNHPyul/JIqtD8SEXdSXTnuRRNWrTQKXg1Nk1ZEdJRSdkfELKrLR764VNeilSYF54A1md1YX/z9GOAKw1eTjSNgSUriHLAkJTGAJSmJASxJSQxgSUpiAEtSkv8DL/RuX9wibQEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new_without_outliers['Age'])\n", + "ax.set_title('Boxplot by Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "1bdd5784", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new_without_outliers['SibSp'])\n", + "ax.set_title('Boxplot by Siblings and Spouses')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "34edb3ae", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new_without_outliers['Parch'])\n", + "ax.set_title('Boxplot by Parents and Children')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "b8ebe593", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.boxplot(x=titanic_new_without_outliers['Fare'])\n", + "ax.set_title('Boxplot by Fare')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c16167a3", + "metadata": {}, + "source": [ + "Although we still observed that there are a few outliers, that may be newly generated outliers, now looks much better." + ] + }, + { + "cell_type": "markdown", + "id": "f7c61c16", + "metadata": {}, + "source": [ + "## Removing columns that aren't important for our models" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "b5c73449", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 825 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 825 non-null int64 \n", + " 1 Survived 825 non-null int64 \n", + " 2 Pclass 825 non-null int64 \n", + " 3 Name 825 non-null object \n", + " 4 Sex_male 825 non-null float64\n", + " 5 Age 825 non-null float64\n", + " 6 SibSp 825 non-null int64 \n", + " 7 Parch 825 non-null int64 \n", + " 8 Ticket 825 non-null object \n", + " 9 Fare 825 non-null float64\n", + " 10 Embarked_Q 825 non-null float64\n", + " 11 Embarked_S 825 non-null float64\n", + "dtypes: float64(5), int64(5), object(2)\n", + "memory usage: 83.8+ KB\n" + ] + } + ], + "source": [ + "titanic_new_without_outliers.info()" + ] + }, + { + "cell_type": "markdown", + "id": "bca30cc7", + "metadata": {}, + "source": [ + "For our first modeling attempt we are not going to use the following variables: \n", + "- `PassengerId`\n", + "- `Name` \n", + "- `Ticket` \n", + "\n", + "Some of them are redundant for our model or required future cleaning. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "60e1b130", + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_drop = ['PassengerId', 'Name', 'Ticket']\n", + "\n", + "titanic_new_without_outliers.drop(columns_to_drop, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "8b113ca5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 825 entries, 0 to 890\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Survived 825 non-null int64 \n", + " 1 Pclass 825 non-null int64 \n", + " 2 Sex_male 825 non-null float64\n", + " 3 Age 825 non-null float64\n", + " 4 SibSp 825 non-null int64 \n", + " 5 Parch 825 non-null int64 \n", + " 6 Fare 825 non-null float64\n", + " 7 Embarked_Q 825 non-null float64\n", + " 8 Embarked_S 825 non-null float64\n", + "dtypes: float64(5), int64(4)\n", + "memory usage: 64.5 KB\n" + ] + } + ], + "source": [ + "titanic_new_without_outliers.info()" + ] + }, + { + "cell_type": "markdown", + "id": "61f3010a", + "metadata": {}, + "source": [ + "# Exporting data as `.csv`" + ] + }, + { + "cell_type": "markdown", + "id": "2538c472", + "metadata": {}, + "source": [ + "## Exporting data for visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "160e0434", + "metadata": {}, + "outputs": [], + "source": [ + "titanic_new.to_csv('./data/train_cleaned_knn_imputation.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "dbce7d4c", + "metadata": {}, + "source": [ + "## Exporting data for predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ab667635", + "metadata": {}, + "outputs": [], + "source": [ + "titanic_new_without_outliers.to_csv('./data/train_cleaned_knn_imputation_without_outliers.csv')" + ] + } + ], + "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": "3.9.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Titanic_Predictions_AN.ipynb b/Code/Titanic_Predictions_AN.ipynb new file mode 100644 index 0000000..07a2e79 --- /dev/null +++ b/Code/Titanic_Predictions_AN.ipynb @@ -0,0 +1,10074 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8128cec1", + "metadata": {}, + "source": [ + "**TITANIC - PREDICTIONS - IRONHACK**" + ] + }, + { + "cell_type": "markdown", + "id": "b42382a2", + "metadata": {}, + "source": [ + "# Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "75d75e98", + "metadata": {}, + "outputs": [], + "source": [ + "# Data processing\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Machine Learning models\n", + "from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier\n", + "from sklearn.svm import SVC, LinearSVC\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from catboost import CatBoostClassifier\n", + "from sklearn.ensemble import StackingClassifier\n", + "\n", + "# Model evaluation\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import accuracy_score, cohen_kappa_score, precision_score, recall_score, confusion_matrix, plot_confusion_matrix\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "c50b2032", + "metadata": {}, + "source": [ + "# Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "7b67c5b6", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./data/train_cleaned_knn_imputation_without_outliers.csv', index_col=0)\n", + "titanic = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "80ae57ec", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Sex_male Age SibSp Parch Fare Embarked_Q \\\n", + "0 0 3 1.0 22.0 1 0 7.2500 0.0 \n", + "1 1 1 0.0 38.0 1 0 71.2833 0.0 \n", + "2 1 3 0.0 26.0 0 0 7.9250 0.0 \n", + "3 1 1 0.0 35.0 1 0 53.1000 0.0 \n", + "4 0 3 1.0 35.0 0 0 8.0500 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "886 0 2 1.0 27.0 0 0 13.0000 0.0 \n", + "887 1 1 0.0 19.0 0 0 30.0000 0.0 \n", + "888 0 3 0.0 22.0 1 2 23.4500 0.0 \n", + "889 1 1 1.0 26.0 0 0 30.0000 0.0 \n", + "890 0 3 1.0 32.0 0 0 7.7500 1.0 \n", + "\n", + " Embarked_S \n", + "0 1.0 \n", + "1 0.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + ".. ... \n", + "886 1.0 \n", + "887 1.0 \n", + "888 1.0 \n", + "889 0.0 \n", + "890 0.0 \n", + "\n", + "[825 rows x 9 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "25cd4c35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 825 entries, 0 to 890\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Survived 825 non-null int64 \n", + " 1 Pclass 825 non-null int64 \n", + " 2 Sex_male 825 non-null float64\n", + " 3 Age 825 non-null float64\n", + " 4 SibSp 825 non-null int64 \n", + " 5 Parch 825 non-null int64 \n", + " 6 Fare 825 non-null float64\n", + " 7 Embarked_Q 825 non-null float64\n", + " 8 Embarked_S 825 non-null float64\n", + "dtypes: float64(5), int64(4)\n", + "memory usage: 64.5 KB\n" + ] + } + ], + "source": [ + "titanic.info()" + ] + }, + { + "cell_type": "markdown", + "id": "a1688ae0", + "metadata": {}, + "source": [ + "# Modeling" + ] + }, + { + "cell_type": "markdown", + "id": "1d0605e2", + "metadata": {}, + "source": [ + "Since this is traditional binary classification problem we are going to use classification models. There are so many models, that can perform very well on this task, but this time we are going to use 10 most common and well known models. We are going to compare performance metrics like score for each model and once we identify the best model, we are going to use hyperparameter tuning to further boost the performance of the best model. \n", + " \n", + "The models we are going to use are: \n", + "\n", + "- Logistic regression \n", + "- Support vector machines \n", + "- K-nearest neighbors \n", + "- Gaussian naive bayes \n", + "- Perceptron \n", + "- Linear SVC \n", + "- Stochastic gradient descent \n", + "- Decision tree \n", + "- Random forest \n", + "- CatBoost " + ] + }, + { + "cell_type": "markdown", + "id": "f7559c48", + "metadata": {}, + "source": [ + "## Preprocessing data" + ] + }, + { + "cell_type": "markdown", + "id": "c4c17be8", + "metadata": {}, + "source": [ + "### Splitting the data" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "7d9e4069", + "metadata": {}, + "outputs": [], + "source": [ + "# defining x and y\n", + "\n", + "X = titanic.drop('Survived', axis=1)\n", + "y = titanic['Survived']\n", + "\n", + "# split into 70 / 30\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=156)" + ] + }, + { + "cell_type": "markdown", + "id": "eb4e3ddd", + "metadata": {}, + "source": [ + "### Scaling the data" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "50e26ca8", + "metadata": {}, + "outputs": [], + "source": [ + "# defining the scaler\n", + "\n", + "scaler = StandardScaler() \n", + "\n", + "# training the scaler\n", + "\n", + "scaler.fit(X_train)\n", + "\n", + "# perform transformation\n", + "\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "158202c7", + "metadata": {}, + "source": [ + "## Making predictions" + ] + }, + { + "cell_type": "markdown", + "id": "ec382115", + "metadata": {}, + "source": [ + "### Logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "924fdfc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8145580589254766, 0.7620967741935484, 0.6087838137542535)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "log_reg = LogisticRegression()\n", + "\n", + "# train the model \n", + "log_reg.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = log_reg.predict(X_train_scaled)\n", + "y_pred_test = log_reg.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_log_reg_train = accuracy_score(y_train, y_pred_train)\n", + "acc_log_reg_test = accuracy_score(y_test, y_pred_test)\n", + "log_reg_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_log_reg_train, acc_log_reg_test, log_reg_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "a2c35bfd", + "metadata": {}, + "source": [ + "### Support vector machines" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "b0ce9a37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.854419410745234, 0.7983870967741935, 0.688820976398983)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "svc = SVC()\n", + "\n", + "# train the model \n", + "svc.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = svc.predict(X_train_scaled)\n", + "y_pred_test = svc.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_svc_train = accuracy_score(y_train, y_pred_train)\n", + "acc_svc_test = accuracy_score(y_test, y_pred_test)\n", + "svc_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_svc_train, acc_svc_test, svc_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "5b60ddfa", + "metadata": {}, + "source": [ + "### K-nearest neighbors" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "37f0d63a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8700173310225303, 0.7540322580645161, 0.728695292369614)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "\n", + "# train the model \n", + "knn.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train_knn = knn.predict(X_train_scaled)\n", + "y_pred_test_knn = knn.predict(X_test_scaled)\n", + "\n", + "\n", + "# get score\n", + "acc_knn_train = accuracy_score(y_train, y_pred_train_knn)\n", + "acc_knn_test = accuracy_score(y_test,y_pred_test_knn)\n", + "knn_kappa = cohen_kappa_score(y_train, y_pred_train_knn)\n", + "\n", + "acc_knn_train, acc_knn_test, knn_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "6ad1991d", + "metadata": {}, + "source": [ + "### Gaussian naive bayes" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "4d8c5e4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7833622183708839, 0.7338709677419355, 0.5457579937146132)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "gaussian = GaussianNB()\n", + "\n", + "# train the model \n", + "gaussian.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = gaussian.predict(X_train_scaled)\n", + "y_pred_test = gaussian.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_gaussian_train = accuracy_score(y_train, y_pred_train)\n", + "acc_gaussian_test = accuracy_score(y_test, y_pred_test)\n", + "gaussian_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_gaussian_train, acc_gaussian_test, gaussian_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "5b4898bd", + "metadata": {}, + "source": [ + "### Perceptron" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "457d10ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7417677642980935, 0.6854838709677419, 0.44327019588797156)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "perceptron = Perceptron()\n", + "\n", + "# train the model \n", + "perceptron.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = perceptron.predict(X_train_scaled)\n", + "y_pred_test = perceptron.predict(X_test_scaled)\n", + "\n", + "\n", + "# get score\n", + "acc_perceptron_train = accuracy_score(y_train, y_pred_train)\n", + "acc_perceptron_test = accuracy_score(y_test, y_pred_test)\n", + "p_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_perceptron_train, acc_perceptron_test, p_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "4cc0fca4", + "metadata": {}, + "source": [ + "### Linear SVC" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "b8c3e0c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8093587521663779, 0.7661290322580645, 0.5975065317200619)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "linear_svc = LinearSVC()\n", + "\n", + "# train the model \n", + "linear_svc.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = linear_svc.predict(X_train_scaled)\n", + "y_pred_test = linear_svc.predict(X_test_scaled)\n", + "\n", + "\n", + "# get score\n", + "acc_linear_svc_train = accuracy_score(y_train, y_pred_train)\n", + "acc_linear_svc_test = accuracy_score(y_test, y_pred_test)\n", + "linear_svc_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_linear_svc_train, acc_linear_svc_test, linear_svc_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "30fccb0a", + "metadata": {}, + "source": [ + "### Stochastic gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "d876f7c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7521663778162911, 0.7096774193548387, 0.47715967632577805)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "sgd = SGDClassifier()\n", + "\n", + "# train the model \n", + "sgd.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = sgd.predict(X_train_scaled)\n", + "y_pred_test = sgd.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_sgd_train = accuracy_score(y_train, y_pred_train)\n", + "acc_sgd_test = accuracy_score(y_test, y_pred_test)\n", + "sgd_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_sgd_train, acc_sgd_test, sgd_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "4c3bc588", + "metadata": {}, + "source": [ + "### Decision tree" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "0fdf74e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9861351819757366, 0.7701612903225806, 0.9708173174185717)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "decision_tree = DecisionTreeClassifier()\n", + "\n", + "# train the model \n", + "decision_tree.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = decision_tree.predict(X_train_scaled)\n", + "y_pred_test = decision_tree.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_decision_tree_train = accuracy_score(y_train, y_pred_train)\n", + "acc_decision_tree_test = accuracy_score(y_test, y_pred_test)\n", + "dt_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_decision_tree_train, acc_decision_tree_test, dt_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "d0427e37", + "metadata": {}, + "source": [ + "### Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "e91aa8fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9861351819757366, 0.7782258064516129, 0.9709506488275793)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "random_forest = RandomForestClassifier()\n", + "\n", + "# train the model \n", + "random_forest.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train = random_forest.predict(X_train_scaled)\n", + "y_pred_test = random_forest.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_random_forest_train = accuracy_score(y_train, y_pred_train)\n", + "acc_random_forest_test = accuracy_score(y_test, y_pred_test)\n", + "rf_kappa = cohen_kappa_score(y_train, y_pred_train)\n", + "\n", + "acc_random_forest_train, acc_random_forest_test, rf_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "35e2bd5f", + "metadata": {}, + "source": [ + "### CatBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "cad39ef8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate set to 0.008146\n", + "0:\tlearn: 0.6877333\ttotal: 2.59ms\tremaining: 2.58s\n", + "1:\tlearn: 0.6822041\ttotal: 4.74ms\tremaining: 2.36s\n", + "2:\tlearn: 0.6771627\ttotal: 7.03ms\tremaining: 2.34s\n", + "3:\tlearn: 0.6719147\ttotal: 10.2ms\tremaining: 2.55s\n", + "4:\tlearn: 0.6670557\ttotal: 12.3ms\tremaining: 2.45s\n", + "5:\tlearn: 0.6623633\ttotal: 15.2ms\tremaining: 2.52s\n", + "6:\tlearn: 0.6574019\ttotal: 18.2ms\tremaining: 2.58s\n", + "7:\tlearn: 0.6534231\ttotal: 19.9ms\tremaining: 2.47s\n", + "8:\tlearn: 0.6485163\ttotal: 23ms\tremaining: 2.54s\n", + "9:\tlearn: 0.6438706\ttotal: 26.5ms\tremaining: 2.62s\n", + "10:\tlearn: 0.6390681\ttotal: 29ms\tremaining: 2.61s\n", + "11:\tlearn: 0.6347548\ttotal: 31.2ms\tremaining: 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84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# establishing a model \n", + "catboost = CatBoostClassifier()\n", + "\n", + "# train the model \n", + "catboost.fit(X_train_scaled, y_train)\n", + "\n", + "# make prediction\n", + "y_pred_train_catboost = catboost.predict(X_train_scaled)\n", + "y_pred_test_catboost = catboost.predict(X_test_scaled)\n", + "\n", + "# get score\n", + "acc_catboost_train = accuracy_score(y_train, y_pred_train_catboost)\n", + "acc_catboost_test = accuracy_score(y_test, y_pred_test_catboost)\n", + "catboost_kappa = cohen_kappa_score(y_train, y_pred_train_catboost)\n", + "\n", + "acc_catboost_train, acc_catboost_test, catboost_kappa" + ] + }, + { + "cell_type": "markdown", + "id": "302eac42", + "metadata": {}, + "source": [ + "## Model evaluation and hyperparameter tuning " + ] + }, + { + "cell_type": "markdown", + "id": "140a84bf", + "metadata": {}, + "source": [ + "The idea is to assess the performance of these models and then select one which has the highest prediction accuracy" + ] + }, + { + "cell_type": "markdown", + "id": "f394a604", + "metadata": {}, + "source": [ + "### Training accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "10b29003", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTrain Accuracy scoreTest Accuracy scoreKappa score
0Random Forest0.9861350.7782260.970951
1Decision Tree0.9861350.7701610.970817
2CatBoost0.9168110.8225810.823008
3KNN0.8700170.7540320.728695
4Support Vector Machines0.8544190.7983870.688821
5Logistic Regression0.8145580.7620970.608784
6Linear SVC0.8093590.7661290.597507
7Naive Bayes0.7833620.7338710.545758
8Stochastic Gradient Decent0.7521660.7096770.477160
9Perceptron0.7417680.6854840.443270
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" + ], + "text/plain": [ + " Model Train Accuracy score Test Accuracy score \\\n", + "0 Random Forest 0.986135 0.778226 \n", + "1 Decision Tree 0.986135 0.770161 \n", + "2 CatBoost 0.916811 0.822581 \n", + "3 KNN 0.870017 0.754032 \n", + "4 Support Vector Machines 0.854419 0.798387 \n", + "5 Logistic Regression 0.814558 0.762097 \n", + "6 Linear SVC 0.809359 0.766129 \n", + "7 Naive Bayes 0.783362 0.733871 \n", + "8 Stochastic Gradient Decent 0.752166 0.709677 \n", + "9 Perceptron 0.741768 0.685484 \n", + "\n", + " Kappa score \n", + "0 0.970951 \n", + "1 0.970817 \n", + "2 0.823008 \n", + "3 0.728695 \n", + "4 0.688821 \n", + "5 0.608784 \n", + "6 0.597507 \n", + "7 0.545758 \n", + "8 0.477160 \n", + "9 0.443270 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# initialize dataframe with performance metrics for each model\n", + "models = pd.DataFrame({'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n", + " 'Random Forest', 'Naive Bayes', 'Perceptron', 'Stochastic Gradient Decent', \n", + " 'Linear SVC', 'Decision Tree', 'CatBoost'],\n", + " \n", + " 'Train Accuracy score': [acc_svc_train, acc_knn_train, acc_log_reg_train, acc_random_forest_train,\n", + " acc_gaussian_train, acc_perceptron_train, acc_sgd_train, acc_linear_svc_train,\n", + " acc_decision_tree_train, acc_catboost_train],\n", + " \n", + " 'Test Accuracy score': [acc_svc_test, acc_knn_test, acc_log_reg_test, acc_random_forest_test,\n", + " acc_gaussian_test, acc_perceptron_test, acc_sgd_test, acc_linear_svc_test,\n", + " acc_decision_tree_test, acc_catboost_test],\n", + " \n", + " 'Kappa score': [svc_kappa, knn_kappa, log_reg_kappa, rf_kappa, gaussian_kappa,\n", + " p_kappa, sgd_kappa, linear_svc_kappa, dt_kappa, catboost_kappa]})\n", + "\n", + "\n", + "# sort data frame from highest to lowest train score\n", + "\n", + "models.sort_values(by='Train Accuracy score', ascending=False, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "fd82ae9e", + "metadata": {}, + "source": [ + "**Which model has the best performance?** \n", + "What is the difference between train and test score?" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "1fad552b", + "metadata": {}, + "outputs": [], + "source": [ + "# compute absolute difference between train and test \n", + "absolute_difference_train_test = abs(models['Train Accuracy score'] - models['Test Accuracy score'])\n", + "\n", + "\n", + "# add absolute difference in our models dataframe\n", + "models['Train / Test AD'] = absolute_difference_train_test" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "203a08a0", + "metadata": {}, + "outputs": [], + "source": [ + "# round the number to the second number\n", + "\n", + "m = models[['Train Accuracy score', 'Test Accuracy score', 'Kappa score', 'Train / Test AD']].apply(lambda x: round(x,2))\n", + "models = pd.concat([models[['Model']],m],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "0a5282e5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTrain Accuracy scoreTest Accuracy scoreKappa scoreTrain / Test AD
0Stochastic Gradient Decent0.750.710.480.04
1Linear SVC0.810.770.600.04
2Logistic Regression0.810.760.610.05
3Naive Bayes0.780.730.550.05
4Support Vector Machines0.850.800.690.06
5Perceptron0.740.690.440.06
6CatBoost0.920.820.820.09
7KNN0.870.750.730.12
8Random Forest0.990.780.970.21
9Decision Tree0.990.770.970.22
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" + ], + "text/plain": [ + " Model Train Accuracy score Test Accuracy score \\\n", + "0 Stochastic Gradient Decent 0.75 0.71 \n", + "1 Linear SVC 0.81 0.77 \n", + "2 Logistic Regression 0.81 0.76 \n", + "3 Naive Bayes 0.78 0.73 \n", + "4 Support Vector Machines 0.85 0.80 \n", + "5 Perceptron 0.74 0.69 \n", + "6 CatBoost 0.92 0.82 \n", + "7 KNN 0.87 0.75 \n", + "8 Random Forest 0.99 0.78 \n", + "9 Decision Tree 0.99 0.77 \n", + "\n", + " Kappa score Train / Test AD \n", + "0 0.48 0.04 \n", + "1 0.60 0.04 \n", + "2 0.61 0.05 \n", + "3 0.55 0.05 \n", + "4 0.69 0.06 \n", + "5 0.44 0.06 \n", + "6 0.82 0.09 \n", + "7 0.73 0.12 \n", + "8 0.97 0.21 \n", + "9 0.97 0.22 " + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sort by lowest absolute difference\n", + "models.sort_values(by='Train / Test AD', ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "186d3ae2", + "metadata": {}, + "source": [ + "Highest kappa score?" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "e62a19f2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTrain Accuracy scoreTest Accuracy scoreKappa scoreTrain / Test AD
0Random Forest0.990.780.970.21
1Decision Tree0.990.770.970.22
2CatBoost0.920.820.820.09
3KNN0.870.750.730.12
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5Logistic Regression0.810.760.610.05
6Linear SVC0.810.770.600.04
7Naive Bayes0.780.730.550.05
8Stochastic Gradient Decent0.750.710.480.04
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" + ], + "text/plain": [ + " Model Train Accuracy score Test Accuracy score \\\n", + "0 Random Forest 0.99 0.78 \n", + "1 Decision Tree 0.99 0.77 \n", + "2 CatBoost 0.92 0.82 \n", + "3 KNN 0.87 0.75 \n", + "4 Support Vector Machines 0.85 0.80 \n", + "5 Logistic Regression 0.81 0.76 \n", + "6 Linear SVC 0.81 0.77 \n", + "7 Naive Bayes 0.78 0.73 \n", + "8 Stochastic Gradient Decent 0.75 0.71 \n", + "9 Perceptron 0.74 0.69 \n", + "\n", + " Kappa score Train / Test AD \n", + "0 0.97 0.21 \n", + "1 0.97 0.22 \n", + "2 0.82 0.09 \n", + "3 0.73 0.12 \n", + "4 0.69 0.06 \n", + "5 0.61 0.05 \n", + "6 0.60 0.04 \n", + "7 0.55 0.05 \n", + "8 0.48 0.04 \n", + "9 0.44 0.06 " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models.sort_values(by='Kappa score', ascending=False, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "9a2dc374", + "metadata": {}, + "outputs": [], + "source": [ + "models.to_csv('./data/models_score.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "993dfc06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTrain Accuracy scoreTest Accuracy scoreKappa scoreTrain / Test AD
0Support Vector Machines0.850.800.690.06
1KNN0.870.750.730.12
2Logistic Regression0.810.760.610.05
3Random Forest0.990.780.970.21
4Naive Bayes0.780.730.550.05
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6Stochastic Gradient Decent0.750.710.480.04
7Linear SVC0.810.770.600.04
8Decision Tree0.990.770.970.22
9CatBoost0.920.820.820.09
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" + ], + "text/plain": [ + " Model Train Accuracy score Test Accuracy score \\\n", + "0 Support Vector Machines 0.85 0.80 \n", + "1 KNN 0.87 0.75 \n", + "2 Logistic Regression 0.81 0.76 \n", + "3 Random Forest 0.99 0.78 \n", + "4 Naive Bayes 0.78 0.73 \n", + "5 Perceptron 0.74 0.69 \n", + "6 Stochastic Gradient Decent 0.75 0.71 \n", + "7 Linear SVC 0.81 0.77 \n", + "8 Decision Tree 0.99 0.77 \n", + "9 CatBoost 0.92 0.82 \n", + "\n", + " Kappa score Train / Test AD \n", + "0 0.69 0.06 \n", + "1 0.73 0.12 \n", + "2 0.61 0.05 \n", + "3 0.97 0.21 \n", + "4 0.55 0.05 \n", + "5 0.44 0.06 \n", + "6 0.48 0.04 \n", + "7 0.60 0.04 \n", + "8 0.97 0.22 \n", + "9 0.82 0.09 " + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models" + ] + }, + { + "cell_type": "markdown", + "id": "8408b857", + "metadata": {}, + "source": [ + "### Compare y and y_predicted for train and test " + ] + }, + { + "cell_type": "markdown", + "id": "4ec2477a", + "metadata": {}, + "source": [ + "Based on two highest kappa score (excluding random forest and decision tree, due to very high difference in train and test)" + ] + }, + { + "cell_type": "markdown", + "id": "750f65e2", + "metadata": {}, + "source": [ + "#### Train" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "af03c2e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y Real CatBoost KNN\n", + "348 1 1 0\n", + "125 1 1 0\n", + "432 1 1 0\n", + "583 0 0 1\n", + "423 0 0 1\n", + "332 0 0 1\n", + "854 0 1 0\n", + "426 1 1 0\n", + "519 0 0 1\n", + "665 0 0 1\n", + "72 0 0 1\n", + "133 1 1 0\n", + "43 1 1 0\n", + "381 1 1 0\n", + "402 0 0 1\n", + "659 0 0 1\n", + "293 0 0 1\n", + "505 0 1 0\n", + "100 0 0 1\n", + "789 0 0 1\n", + "882 0 0 1\n", + "817 0 0 1\n", + "483 1 0 1\n", + "240 0 0 1\n", + "328 1 0 1" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_models_test[compare_models_test['CatBoost'] != compare_models_test['KNN']]" + ] + }, + { + "cell_type": "markdown", + "id": "dad4db20", + "metadata": {}, + "source": [ + "## Combining two models" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "f7168a9c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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"y_test_pred = stack_model.predict(X_test_scaled)\n", + "\n", + "# Stack model performance\n", + "\n", + "acc_stack_model_train = accuracy_score(y_train, y_train_pred)\n", + "acc_stack_model_test = accuracy_score(y_test, y_test_pred)\n", + "stack_model_kappa = cohen_kappa_score(y_train, y_train_pred)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f595c50d", + "metadata": {}, + "source": [ + "### Evaluating combined model" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "3891b12a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "StackingClassifier(estimators=[('knn', KNeighborsClassifier()),\n", + " ('CatBoost',\n", + " )])" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stack_model" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "8e4d8912", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9150779896013865, 0.8104838709677419, 0.8197392330007331)" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acc_stack_model_train, acc_stack_model_test, stack_model_kappa" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "26c72c8e", + "metadata": {}, + "outputs": [], + "source": [ + "stack_models = pd.DataFrame({\n", + " 'Train score': [acc_stack_model_train],\n", + "\n", + " 'Test score': [acc_stack_model_test],\n", + "\n", + " 'Kappa score': [stack_model_kappa],\n", + "\n", + " 'Train / Test AD': [abs(acc_stack_model_train - acc_stack_model_test)]})" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "987057e2", + "metadata": {}, + "outputs": [], + "source": [ + "# round the number to the second number\n", + "\n", + "stack_models = stack_models[['Train score', 'Test score', 'Kappa score', 'Train / Test AD']].apply(lambda x: round(x,2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "240adadc", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Train scoreTest scoreKappa scoreTrain / Test AD
00.920.810.820.1
\n", + "
" + ], + "text/plain": [ + " Train score Test score Kappa score Train / Test AD\n", + "0 0.92 0.81 0.82 0.1" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stack_models" + ] + }, + { + "cell_type": "markdown", + "id": "ae5e2fd9", + "metadata": {}, + "source": [ + "### Confusion matrix" + ] + }, + { + "cell_type": "markdown", + "id": "43dc007f", + "metadata": {}, + "source": [ + "#### Confusion matrix for the train set" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "f2f41553", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[334 15]\n", + " [ 34 194]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(confusion_matrix(y_train, y_train_pred))\n", + "plot_confusion_matrix(stack_model, X_train_scaled, y_train, values_format='d')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0229a5e6", + "metadata": {}, + "source": [ + "#### Confusion matrix for the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "160b4214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[132 24]\n", + " [ 23 69]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(confusion_matrix(y_test, y_test_pred))\n", + "plot_confusion_matrix(stack_model, X_test_scaled, y_test, values_format='d')\n", + "plt.show()" + ] + } + ], + "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": "3.9.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Code/Titanic_Visualizations_AN.ipynb b/Code/Titanic_Visualizations_AN.ipynb new file mode 100644 index 0000000..1e540af --- /dev/null +++ b/Code/Titanic_Visualizations_AN.ipynb @@ -0,0 +1,793 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "684a805d", + "metadata": {}, + "source": [ + "**TITANIC - VISUALIZATION - IRONHACK**" + ] + }, + { + "cell_type": "markdown", + "id": "2eecbe89", + "metadata": {}, + "source": [ + "# Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d4a32a7f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d5c1cc23", + "metadata": {}, + "source": [ + "# Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b870a7a3", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./data/train.csv')\n", + "df2 = pd.read_csv('./data/train_cleaned_knn_imputation.csv')\n", + "\n", + "titanic = df.copy()\n", + "titanic_new = df2.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7acc56c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "891" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic['Survived'].value_counts().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d6497279", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qamp0yy23qLKyUrNnz9Zrr712zEKOwWQxDMOI2bsDAAAMMr/fr71792rs2LFyu91mx4mpwfpamcMHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOs3QBAEDyuC1riD9fU58u/+CDD3TXXXdp7dq1qqio0HPPPadLLrkkNtmOwAgfAADAEGlra9OsWbP0xz/+cUg/LyN8AAAAQ2Tp0qVaunTpkH9eRvgAAACSHIUPAAAgyVH4AAAAkhyFDwAAIMlR+AAAAJIcq3QBAACGSGtrq3bt2tX5871792rDhg3Kzc3VqFGjYvZ5KXwAACB59HEj5KG2Zs0anXHGGZ0/v/HGGyVJ1157rR588MGYfV4KHwAAwBA5/fTTZRjGkH9e5vABAAAkOQofAABAkqPwAQAAJDkKH3CE2267TRaL5agfU6ZMOeY6wzC0dOlSWSwWPf/880c99/bbb+vkk09WZmamioqKdNNNNykcDg/RVwAAwLFYtAF8zgknnKC33nqr8+d2+7G/Te6++25ZLJZjHt+4caMuuOAC3Xzzzfrb3/6msrIyfec731EkEtGvfvWrmOYGgFRjxuKHoTZYXyOFD/gcu92uoqKiLp/fsGGDfv3rX2vNmjUaPnz4Uc898cQTmjlzpm655RZJ0oQJE/TLX/5SV1xxhW699VZlZmbGNDsApAKHwyFJam9vl8fjMTlNbLW3t0v67GvuLwof8Dk7d+7UiBEj5Ha7tWjRIt1xxx2dm2G2t7frK1/5iv74xz8etxQGAgG53e6jHvN4PPL7/Vq7dq1OP/30ofgSACCp2Ww2ZWdnq7q6WpKUlpZ23LsuicwwDLW3t6u6ulrZ2dmy2WwDej8KH3CEhQsX6sEHH9TkyZNVUVGh22+/XUuWLNGWLVuUmZmpG264QSeffLIuvvji477+vPPO0913363HHntMV1xxhSorK/X//t//kyRVVFQM5ZcCAEnt8D+6D5e+ZJWdnd3tXafeovABR1i6dGnnxzNnztTChQs1evRoPfnkkyooKNA777yj9evXd/n6c889V3fddZe+853v6JprrpHL5dLPfvYzffjhh7JaWSMFAIPFYrFo+PDhGjZsmEKhkNlxYsLhcAx4ZO8wi5EKMx6BAZg/f77OPvts+Xw+/f73vz+quEUiEVmtVi1ZskTvvfde5+OGYaiiokI5OTnat2+fpk2bptWrV2v+/PkmfAUAgFTHCB/QjdbWVu3evVvXXHONrrjiCn3jG9846vkZM2bot7/9rZYtW3bU4xaLRSNGjJAkPfbYYyopKdGcOXOGLDcAAEei8AFH+MEPfqBly5Zp9OjRKi8v16233iqbzaarrrpKBQUFx51HMWrUKI0dO7bz53fddZfOP/98Wa1WPfvss7rzzjv15JNPDtqwPAAAfUXhA45QWlqqq666SnV1dSooKNApp5yilStXqqCgoNfv8eqrr+q///u/FQgENGvWLL3wwgtHzQ0EAGCoMYcPAAAgybFsEAAAIMlxSxdAQguGo2ryhdTsD6nZF1KzP6xAKCJD0mf3Lzo+OPzzww9/9vPPbnS47DZluu3KdNvldTsOfeyQzZpcm7oCSC0UPgBxoy0QVmmDT2WN7apqDnQUuc4yF+4sdU2Hil2zL6RAODok2dKcts7yl+m2K8N1ZCHs+Dg/06WiLLeGZ7k13OtRVtrAjkICgMHCHD4AQ6bZH1JpvU+lDe0qa/SptOHojxvbk2vz1DSnTUVetwq9bo3I9qgk16OSnDSNyktTSU6aCr2upDsOCkB8ovABGFT+UEQ7qlq0vaJZn1a26kD94ULXrhZ/2Ox4ccVpt2pktkcluWmaVJihqcO9mlLk1cTCDDlsTLEGMHgofAD6rbY1oG3lzdpW0aztFc3aVt6sPbVtikT5Y2UgHDaLxhd0FMCpwzM1pcirqcO9Ksh0mR0NQIKi8AHoUSRqaG9tq7aWN2t7RUtnwatpCZgdLaXkZzgPlUCvphRlaupwryYMYzQQQM8ofACO0R4Ma82+Bq3cU6dVe+u1tbxJ/tDQLI5A37gdVs0uydbCsXlaOC5Xc0blyO3gVBcAR6PwAVBbIKyP99Vr1d56rdxTpy1lTQpF+KMhETltVs0cmaWF43K1YGye5o3OUbqLDRmAVEfhA1JQ66GCt3JPnVbtqdeWsiaFmXeXlOxWi04oztLCsblaODZX88fmyutmuxgg1VD4gBTgD0W0Yk+dVu6u6xjBK29mYUWKslqkKUVenTQuT6dPLtCi8XnMAQRSAIUPSFJNvpDe+aRKr2+p0gc7a9QejJgdCXEo023X6ZOH6ZxphTpjcoEyGf0DkhKFD0gilU1+vbGtUq9vrdSqPfXcpkWfOGwWnTQuT+dOK9TZ0wo1PMtjdiQAg4TCByS4nVUtemNbld7YWqlNZU3idzQGg8UizSjO0jlTC3XOCYWaUuQ1OxKAAaDwAQnGMAytO9CoN7ZV6s2tVdpT22Z2JKSAUblpOmdaoc6ZVqgFY3JltXIkHJBIKHxAgthS1qRn15XppU3lqmbDY5ioyOvWF+cU6/K5IzWuIMPsOAB6gcIHxLHqFr9eWF+uZ9aV6pPKFrPjAMeYOzpHl80dqYtmDmfBBxDHKHxAnAmEI3pzW5WeWVuqD3bWsn0KEoLHYdP504t02dyROnl8niwWbvkC8YTCB8SJHVUtemz1AT23vkyN7SGz4wD9Vpzt0aVzR+ryuSNVkptmdhwAovABpvIFI3pxU7keX31A6w40mh0HGFQWi7RgTK4un1eiC2YUKc3JEW+AWSh8gAl2VrXoweX79I8N5WoJhM2OA8RcutOmS+eO1NdPGavReelmxwFSDoUPGELLd9XqLx/u0fs7atgvDynJapHOnlqob546TvPH5JodB0gZFD4gxsKRqF7eXKH7PtyjLWXNZscB4saskmx945SxumDGcNnY1w+IKQofECOtgbAeX31AD3y0T2WNPrPjAHGrONuj6xeP0ZULRinDxTw/IBYofMAgq2zy64GP9urR1QfU4md+HtBbmW67rpxfousXj9WIbM7xBQYThQ8YJNsrmnXfB3v04qZyhSL8tgL6y2616IIZw/XNJeM0Y2SW2XGApEDhAwboo121+vP7u/XhzlqzowBJ56RxufrPcyezwAMYIAof0E/rDzTol699qhV76syOAiS90ycX6AfnTtb0Ykb8gP6g8AF9tKOqRb96/VO9sa3K7ChASrFYpAumD9eN507S+IIMs+MACYXCB/TSwfp2/fatHXp+fZk43hYwj81q0RdPLNZ/nD1RI3M4ug3oDQof0IPa1oD+8M4uPbrqgIKRqNlxABzitFl11YIS/euZE1WQ6TI7DhDXKHxAF5r9Id33wR7d/8+9agtGzI4DoAseh03XLR6j75w6XllpDrPjAHGJwgd8jj8U0UPL9+ne93ersT1kdhwAvZTptutbS8bp60vGKs3JBs7AkSh8wCHRqKEn1xzU3W/tVGWz3+w4APopP8Olm86frMvmjpTFwpFtgEThAyRJm0ob9dPnt2hTaZPZUQAMkvljcvT/Lp6uqcO9ZkcBTEfhQ0pr8oX0y9c+0WOrD7DyFkhCNqtF15w0Wv957iRlupnfh9RF4UPKenptqe58dbtqW4NmRwEQYwWZLv146RR9ac5Is6MApqDwIeV8Wtminz2/Rav31ZsdBcAQWzAmVz+/ZLomF2WaHQUYUhQ+pIy2QFh3v7VDD3y0T2Hu3wIpy2616GuLxujGcycpw8VqXqQGCh9SwsubKvTzl7ax+hZAp2GZLt184VRdPLvY7ChAzFH4kNT21rbplhe26MOdtWZHARCnThqXq59fPF0TC7nNi+RF4UNSCkei+tN7u/WHd3cpGOY4NADdc9qs+vezJ+o7p42XzcrefUg+FD4knT01rbrhyY3aeLDR7CgAEszskmz96vJZmjAsw+wowKCi8CGp/H3FPv3PK5/IF+LsWwD947Jb9YNzJ+vrp4yVldE+JAkKH5JCdbNfP3x6k97fUWN2FABJYt7oHP3q8lkak59udhRgwCh8SHivbK7Qzc9tVkN7yOwoAJKMx2HTzRdO1VdPGm12FGBAKHxIWM3+kG59YaueW19mdhQASe6sKcP0i8tmKj/DZXYUoF8ofEhIK3bX6T+f2qDyRvbVAzA08jOc+sWlM3XW1EKzowB9RuFDQgmEI7rrtU/1f//cK37hAjDDVxaO0s8unCaP02Z2FKDXKHxIGNvKm3XDExv0aVWL2VEApLhx+en601fnaEqR1+woQK9Q+JAQnlxzUD97fosCbKIMIE54HDbdeekMjmZDQqDwIa4Fw1Hd9uJWPbrqgNlRAOC4rjt5jG6+cKocNqvZUYAuUfgQtyqb/PrOw2u04WCT2VEAoFvzRufoT1fP0TCv2+wowHFR+BCXVu6p0//v4bWqZ289AAmiINOlP1x1ohaOyzM7CnAMCh/izn0f7Nadr36iCL8yASQYu9Wi/1o6Rd9YMs7sKMBRKHyIG+3BsG58fJ1e28bxaAAS24Uzh+uXl85UustudhRAEoUPcWJfbZuu+78V2tcQMDsKAAyKicMy9Odr5mp8QYbZUQAKH8z35rZK/duj6+QL80sRQHLJdNl11+WzdP70IrOjIMVR+GCaaNTQXa9t058/2MepGQCS2ndPH68fnTdZFovF7ChIURQ+mMIfiujbD63S+7sazI4CAENi2awR+vXls+S0s18fhh6FD0OusT2oq/78obZX+82OAgBD6qRxufrfa+Ypy+MwOwpSDIUPQ+pAbauuuPdDVbZxRBqA1DSpMEMPXr9AI7I9ZkdBCqHwYch8vKtS1z+4Rq1h5rAASG1FXrceuH6+pg73mh0FKYLChyHx3Mod+tELnypkMHcFAKSOFbz/e81cnTwh3+woSAEUPsSUYRj67T9W654VNTLEyB4AHMlhs+iuy2bpkhOLzY6CJEfhQ8xEIhHdcP/b+seuoMRWBABwXBaL9INzJ+t7Z0wwOwqSGIUPMeHzBXTdvW9qVTVFDwB645qTRuv2L5wgq5U/NzH4KHwYdLWNzfrKH9/VjhbOkASAvjhnWqHuuepEuR02s6MgyVD4MKj2lNfq6v/9UBUBp9lRACAhzRmVrQf/ZYG8bvbqw+Ch8GHQbNtbpq/+32rVhyl7ADAQs0qy9fevU/oweCh8GBQfb92lbz6yUY1Rt9lRACApUPowmCh8GLD31mzWvz/zqZoMdo0HgME0a2SW/v6NhZQ+DBiFD/1mGIZefn+1fvLqfjVb0s2OAwBJadbILP3t6ws5fxcDQuFDv0QiET312gf6/96vVqs1w+w4AJDUKH0YKAof+iwQCOpvL7yp333colZbptlxACAlzByZpb9T+tBPFD70ic/v1/8+/g/dtzWiNhuHfgPAUKL0ob84yR695vP79b+PUfYAwCybSpt0zf+tUpMvZHYUJBgKH3rF5/frL4zsAYDpNpU26at/pfShbyh86JE/EOgoe1siarNT9gDAbJvLDpW+dkofeofCh275AwHd98Q/dN+WsFopewAQNzaXNenrD30sfyhidhQkAAofuuQPBHT/Uy/qr5v8arVnmR0HAPA5a/Y36N8fX69olPWX6B6FD8flDwT00FMv6f51zWqy55odBwDQhde3Vun2F7eaHQNxjsKHYwQCQf3t6Zf00Npq1ToLzY4DAOjBQyv268/v7zY7BuIYhQ9HCQSC+vszL+nRj0tV7hpldhwAQC/94rVP9MKGMrNjIE5R+NDpcNl7YtVu7XePNzsOAKAPDEP64VOb9NGuWrOjIA5R+CCpo+w9/OzLenblJ9rtmSJDFrMjAQD6KBiJ6jt/X6tt5c1mR0GcofBB4XBYT7z4uv7x0UbtSp+uKL8sACBhtQTCuv7B1Spr9JkdBXGEv9lTnGEYeuGN9/TS+6u1K3OWgobN7EgAgAGqag7ouvtXszEzOlH4UphhGHrzw5V67o33tTNjptoMp9mRAACDZGd1q775tzUKhNmYGRS+lLZq/WY98eIb2uWZogYjzew4AIBBtnpfvW54YgMbM4PCl6o2f7JTf3vmJe22FKvSyDY7DgAgRl7ZXKnfvLnD7BgwGYUvBe3ZX6r7n3hee3we7bMONzsOACDG/vjeLr25rcrsGDARhS/FVNXW6f4nn9e+Op92uSaYHQcAMAQMQ7rxyQ3aW9tmdhSYhMKXQlpa2/Tgky/o073l2pM5U2GD//0AkCpa/GF9++9r1B4Mmx0FJuBv/BQRDIb08LMva/2WT1SVP1tNEYfZkQAAQ2xHVat+9PQms2PABBS+FBCNRvXsq2/pg1Vr5R92gg4E082OBAAwyUubKvTXD/eYHQNDjMKXAt78cKVefuefcuSP0kZ/ntlxAAAmu/PVT7RqT53ZMTCEKHxJbu3m7Xr65TflSM/Sx6ESzsgFACgcNfS9R9erqtlvdhQMEQpfEiutqNJjz7+iQDiirfYJ8kU5Ng0A0KG2NaDvPrxWoUjU7CgYAhS+JNXW7tPDz76ksspq1WZPU1XQZXYkAECcWXegUT9/aZvZMTAEKHxJKBqN6plX3tKGrZ/KNmKqtrVnmh0JABCn/rZiv55dV2p2DMQYhS8Jvb9yrd78cKW8haO0spVFGgCA7v3kuc3aVt5sdgzEEIUvyezYs19PvfymnJ40rQqNZHNlAECP/KGo/v3x9fKHImZHQYzQBpJIfWOT/v7MS2pqblFZ2gQ1hdlcGQDQOzurW3XX65+aHQMxYjc7AAZHKBTWo8+/qh179itz1FR90pRhdiSYJNxSq8b3HpRvz1oZ4YDs2cOVd8F/yDV8oiSp/dPlatnwqoKVuxT1t2j4db+Xs3Bcj+8b9beq4YO/y7djuSL+Ftm9w5R71jflGT9fktS69V01vv+QjKBP6TPOVu5Z3/wsU1OVqp74mYZfe7esrrTYfOEABuz+j/bqrKnDdPL4fLOjYJBR+JKAYRh6+Z0PtHzNBhWXlOi1FubtpaqIv1WVD/9I7lEzNezy22RNy1K4oVxW92f/AIiG/HKNnKa0Kaeo/rV7evW+RiSkqid+JltalvIv+bHsmXkKN1XL6u44tSXS3qT61+5R3gX/IXt2kaqfvl3u0bOUNmGBJKnujT8p57TrKHtAnDMM6YdPbdKr/7FEXjd3iZIJhS8JrN28XS+99YHyc7O1MVSkdvbbS1nNK5+W3Zuv/Av/o/MxR3bRUddkTD9TUseoW2+1bnpTUX+Lir56lyy2jj827FmFnc+HGytlcaUpfeqpkiT3qJkK1R2UJixQ27b3ZbHalTb55P5+WQCGUFmjT7e9sFW/+fJss6NgEFH4ElzH5sqvKhKNqjW9WLsbGEFJZb5dq+QeO0c1z98h/8EtsmXkKfPEC5Q5+/wBvW/7rlVyjZii+jfvVfvOVbKleZU+7XR5F14qi9Ume26xjFBAwardsnmHKVixQxkzzlbE36rGDx9W4VX/M0hfIYCh8Oz6Mp0zrVBLZww3OwoGCYUvgR3eXLm8qlqjx03Qc3VZZkeCyUKNlQqtf0Xe+ZeocNEVClTsVMPbf5HF5lDGjLP6/b7hxir5mzYpfdrpGnb5bQo3lKv+jXtlRMLKPuUrsrkzlH/hDap96TcywkGlTz9TnnFzVfvK75Q55yKFm6pU/czPpWhYWYu/ovQppwziVw0gFn7y3GbNHZOjYZlus6NgEFD4EpRhGHrhjXe1fsunmjR+tN5rzlWAW7kwDLmKJijntGslSc7C8QrV7lfLhlcGVPhkRGVLy1be+f8qi9UmV9EERVrq1Lz6WWWf8hVJUtqkk5U26bPbtv4DmxWq2afcc76t8r98S/nLfihbeo4q/naj3CXTZUvPHshXCiDGGtpDuunpTXrg+gVmR8EgYFuWBLVh26d668NVGlFUoH2hbB0M8C8wSLaMHDnyRx31mCOvRJHmmgG+b64cuSNksX72jwpHXokibQ0yIqFjrjfCIdW/ca9yz/uewg0VMqIRuUfNkCNvpBy5xQpUsPUDkAje/bRGj646YHYMDAIKXwJqaGrWUy+9oXAkImdmnlY2e82OhDjhKp6mUP3RRySF6stk9w4b4PtOVaihQobx2SHroYYy2TJyZbEdu5Kvafnjco+bI1fRBMmIStHPNnM1omEpymHtQKL4/17epv11bWbHwABR+BKMYRh6/rV3tWd/qcaOGqn3G7MV4jQNHOKdf7EC5Z+qacWTCjWUq23be2rd+Joy5lzYeU3E16Jg1R6Fajv+1R6qL1Wwao8irQ2d19S+9Gs1vP9g588zT7xAUX+LGt76i0L1ZWrf/bGaVjylzBM/e9/DgrUH1PbJh8o+5auSJHvuSMliVcvGN9S++2OF6krlPLQnIID41x6M6MYnNyoSNcyOggGwGIbB/8EEsmr9Zv3poSdUkJerg7YirWpmoQaO1r5rtRrff0ihhnLZswrlnX/JUat0Wze/pbpX7j7mdVmLr1L2KVdLkiof/S/ZswqVf+ENnc8Hyrar/u2/Kli9R/bMPGXMPLdzle5hhmGo6pGb5D3pss49+A5nqn/zXhmRkLKXXKPMWefF4CsHEEs/PG+yvnfGBLNjoJ8ofAmkpq5Bd/35QdXUNyh3xFg9X1OgiCxmxwIApACnzapX/2OJxhdwklMi4l5ggohEInrmlTe1v6xCY0YW66OmLMoeAGDIBCNR3frCVrNjoJ8ofAniozUb9NHHGzRm5AjtCaSrMugyOxIAIMX8c1etXtpUbnYM9AOFLwGUV9XoudfekcvtkistQ6tZlQsAMMn/99J2tQXCZsdAH1H44lwoFNZTL72hiqpajRpRpLUtmfKxwTIAwCSVzX7d/dYOs2Ogjyh8ce69lWu0esMWjR1drIaIU9va0s2OBABIcQ98tE+fVraYHQN9QOGLY/tLy/WPN95VZka60j0erWjKksFCDQCAycJRQz97YYvZMdAHFL441XEr903VNTSpuGiYdrZ7WKgBAIgbq/fW69l1pT1fiLhA4YtTH63ZoPVbP9HYUcUKGVYWagAA4s7/vPKJmv3HnqeN+EPhi0P1jU166a0P5PG45XG7WagBAIhLta0B/fr1T82OgV6g8MUZwzD06rv/VGlFpUqGF6k+ZGehBgAgbj286oC2lDWZHQM9oPDFmR179uu9FWtUNKxANptVy1moAQCIY5FDCzg4qTW+UfjiSCgU1gtvvKd2n195OVks1AAAJIT1Bxr1xMcHzY6BblD44shHazZo47ZPNaZkBAs1AAAJ5ddv7lB7kBM44hWFL058fqHGlrZ0FmoAABJGTUtAf/1wr9kx0AUKXxz4/EINf9Siza0ZZscCAKBP/vLBHtW3Bc2OgeOg8MWBzy/U2NSaoZDB/xoAQGJpDYR1zzs7zY6B46BVmOzzCzXaI1a2YQEAJKxHVh7Qwfp2s2Pgcyh8JjtyoYbFYtGGlkyFGd0DACSoYCSqX7/BZszxhmZhos6FGu6OhRotYZs+aU8zOxYAAAPywsZyba9oNjsGjkDhM9GbH6zoWKgxokiStL4lU1E2WQYAJDjDkH7z5g6zY+AIFD6TlFVW6/2Va1WQlyubzarGsE07fR6zYwEAMCje3FalTaWNZsfAIRQ+k7zz0WrVNTZpWH6uJGlts5cj1AAASYVRvvhB4TPB/tJyffTxeg0fli+LxaK6kF17/W6zYwEAMKje+7RGa/c3mB0DovANOcMw9NaHq9TY3Kq8nGxJ0ppmr8ToHgAgCbFiNz5Q+IbY7v0HtXL9Jo0oKpDFYlF10KGDAUb3AADJafnuOq3YXWd2jJRH4RtChmHojQ9Wqq3dp5wsr6TDo3sAACSvP7zL6Rtmo/ANoU927dWajVtVXDSsc3SvPOgyOxYAADH10a46bStnXz4zUfiGSDQa1evvL1cgGFSWN1OStLk1w+RUAAAMjb9+uMfsCCmNwjdENn+ySxu2fqqRwwslSS1hm/axMhcAkCJe3FSuyia/2TFSFoVvCEQiEb3+/nKFIxFlZqRLkra2pbPvHgAgZYQihh5YvtfsGCmLwjcE1m/9VFs+3dV5hFowatGnnJkLAEgxj606oLZA2OwYKYnCF2OhUFhvvL9cMgylp3UcnfZpe5pCBt96AEBqafaH9fjHB82OkZJoHTG2fst2bd+5RyXFHaN7UaPjdi4AAKnogY/2KhI1zI6Rcih8MRSNRvXeijWS1SKPu2OBxl6/W60Ru8nJAAAwR2mDT69uqTA7Rsqh8MXQJ7v3aduuPSouHNb52Ba2YgEApLj7PmTxxlCj8MWIYRj6cNVaBYMhZaR3LNCoDDhVE3KanAwAAHNtPNio1XvrzY6RUih8MVJWWa11Wz5RYUF+52NbmLsHAIAk6T42Yh5SFL4YWbF2o5qaW5Wb3XFWbnPYpv1stAwAgCTp7e1V2lPTanaMlEHhi4GGpmb98+P1ysvJksXSsbnyFjZaBgCgU9SQ7v+IuXxDhcIXA6s3bFFVTb2G5edJ6thoeScbLQMAcJQX1pfLH4qYHSMlUPgGmT8Q0Hsr1igjI002W8e3d6/Pw0bLAAB8TksgrNe2VJodIyXQQgbZ+i2f6EBZhUYMK+h8bIfPY2IiAADi15NrOHljKFD4BlEkEtF7K9bIZrPJ6XRI6lisURV0mZwMAID4tGJPnQ7Wt5sdI+lR+AbR9l179cnufRpRdMToHnP3AADokmFIz6wrNTtG0qPwDRLDMPTBqnUKhcLKSEs79Ji0k9u5AAB06+m1pTIMzteNJQrfIDlYXqkNWz9R0bC8zsfKg061cW4uAADdKm3wacWeOrNjJDUK3yD5eONWNbW0KifL2/kYt3MBAOidp9dwWzeWKHyDwOf3a9X6zcrJ8nZutByMWjhZAwCAXnp1S6Va/CGzYyQtCt8g2LZzryqqalSQl9P52B6fR2H23gMAoFd8oYhe2lRhdoykRSMZBGs3bVXUMORyOjsfY7EGAAB98xR78sUMhW+AauoatGHbDuUfMbrXxN57AAD02boDjdpV3Wp2jKRE4RugTdt3qL6hSXnZWZ2PcW4uAAD98/RaFm/EAoVvAKLRqFau2yS32yWrteNbyd57AAD037PrShWNsiffYKPwDcCeA6Xavb9Uhfm5nY9VsPceAAD9Vt0S0LoDDWbHSDoUvgHYsPVTtft8ykj/7BYuW7EAADAwb26rMjtC0qHw9ZPP79fKdZuVfcTeexKFDwCAgXqDwjfoKHz9tHXHHlXW1GpY3me3c+tCdrVyOxcAgAHZW9umnVUtZsdIKhS+flq7aatkSE6no/OxA4zuAQAwKBjlG1wUvn6orqvXxm07lJ+XfdTj3M4FAGBwvLG10uwISYXC1w+bt+9UQ1Ozco/Ye68tYlVtyNnNqwAAQG9tKmtSZZPf7BhJg8LXR4Zh6OONW+VyOjv33pMY3QMAYDAZhvTmNkb5BguFr48qa+q072CZ8nKzj3qcwgcAwOBiHt/gofD10c69+9XU0qqszIzOx4JRiyoCnJ0LAMBgWrmnTs3+kNkxkgKFr4+2fLJLNpvtqNu5BwMuRWXp5lUAAKCvQhFD735SbXaMpEDh64OmllZt27lHuTlZRz3OdiwAAMQGt3UHB4WvD3bs2d+xOjfL2/lY1JAOUvgAAIiJ9z+tUTAcNTtGwqPw9cH2nXtkGJLd/tlpGhVBp4IG30YAAGKhNRDW8t21ZsdIeDSVXvIHAtq4bYeyvRlHPc7tXAAAYuujXRS+gaLw9dLu/aWqqa9Xbk72UY+XsToXAICYWrW33uwICY/C10uf7t6nUCgst+uz0zTaI1Y1hh3dvAoAAAzU1vJmtbA9y4BQ+HohEolo3ebtykhPO+rxiiBHqQEAEGuRqKE1+xvMjpHQKHy9cKCsUhXVNcr73O1cNlsGAGBorNxTZ3aEhEbh64VP9+xTm8+v9DTPUY8zwgcAwNBYtYd5fANB4euBYRjasPVTuV0uWSyfnabRHrGqifl7AAAMiS1lTWoLhM2OkbAofD2orqvXvtJy5X3udA1u5wIAMHTCUUNrmcfXbxS+Huw/WK7m1lZlZR69/x63cwEAGFqr9jKPr78ofD3YW1ouw5Cs1qO/VVUUPgAAhhTz+PqPwteNaDSqLZ/sUsbnFmsEohY1hO1dvAoAAMTCptIm+YIRs2MkJApfN6pq61VdW6dsb+ZRj1cHnZIsx38RAACIiWAkqnUHmMfXHxS+bhwoq1BLW7syM9KPeryS27kAAJhiFfvx9QuFrxt79pdKYv4eAADxYiXn6vYLha8L0WhUW3fsPuY4tagh1YTYfw8AADNsPNioSNQwO0bCofB1obquQTV19cdsx1Ifcihi8G0DAMAMgXBUe2pazY6RcGguXThYXqnWdp8y0o+ev1fP6lwAAEy1raLZ7AgJh8LXhYPllYpGDdlsR3+L6rmdCwCAqbZXtJgdIeFQ+Lrwye598niOPT6NwgcAgLm2M8LXZxS+42hqaVVZZZW8GRnHPMctXQAAzEXh6zsK33EcLK9Uc0ubvJlHz99rj1jlj9pMSgUAACSpuiWgutaA2TESCoXvOA6WVyocDsvpOPr2LbdzAQCID8zj6xsK33EcLK+UzXbsSB63cwEAiA/c1u0bCt/nGIahfQfLj9lwWWKEDwCAeEHh6xsK3+c0NDWroblF6WmeY56j8AEAEB/Yi69vKHyfU11br7Z2n9I9Rxe+qCE1cksXAIC4sLumVcFw1OwYCYPC9znVdfUKh8NyOI4ud41hu6KymJQKAAAcKRQxtLOahRu9ReH7nKqaOkmSxXJ0ueN2LgAA8YWVur1H4fucfaXlcruOPWGjgdu5AADEFRZu9B6F7wjBYEjlldVKS3Mf8xwjfAAAxJf9dW1mR0gYFL4j1NQ3qPU4CzYkqYkRPgAA4kppg8/sCAmDwneE6rqOFbppxyl8bRGOVAMAIJ6UNVL4eovCd4Tq2nrJkGy2o78tvohVEVboAgAQV1r8YTX5QmbHSAgUviOUV9bIajv2W9LK6B4AAHGpjNu6vULhO8QwDO09WKY0z7ELNih8AADEJ27r9g6F75CW1jbVNTQe90g15u8BABCfyhrazY6QECh8h9TUN6jN5ztu4WOEDwCA+MQIX+9Q+A5paGpRMBiSy+k85jkKHwAA8YnC1zsUvkOaW1oli+WYI9UkbukCABCv2Iuvdyh8hzS3tEqGcdznGOEDACA+sUq3dyh8h9Q2NMhmO7bYRQzJF+XbBABAPKprC8oXjJgdI+7RZA6pqm3oZv4emy4DABCvmMfXMwqfpGg0qrqGRrlcLNgAACDRUPh6RuGT1NLWLr8/IJfTccxzLNgAACC+MY+vZxQ+dSzYCASDxx3ho/ABABDfGn1BsyPEPQqfpKaWVgUCQbmPM4fPz4INAADiWrMvbHaEuEebUccIX9QwjrtKN0ThAwAgrrX4Q2ZHiHu0GXWM8HUlaLBCFwCAeNbsZ4SvJxQ+SU3NLV0+F2SEDwCAuMYIX89oM5KqauvlPM78PUkKMcIHAEBca/ZR+HpC4ZNUU9dw3C1ZJG7pAgAQ71q4pdujlC98wWBIza1txz1lQ2LRBgAA8a6ZW7o9Svk24wsEFI5EZLcff789RvgAAIhvbMvSs5QvfIFgUJFwWPbjbMkSNaSwkfLfIgAA4povFFE4EjU7RlxL+TYTDIYUjkSPuwcfo3sAACQGtmbpXsoXvkAwqEgXt3SZvwcAQGJga5bupXyjCQRDCkcijPABAJDAmMfXvZQvfP5AQNFoVDbrsd8KRvgAAEgMjPB1L+UbTTDY8QvEYjl2NI8RPgAAEkNLgBG+7qR84QsEg10+xykbAAAkhnDEMDtCXKPwBUOyqItiR+EDACAhRA0KX3cofMGguvwlYuEXDwAAiYDC1z0KXyAoo+vKBwAAEgCFr3spX/jafX5Zj7NgQ1JXN3oBAECc4aCN7qV84Wtr93V5ji4AAEgMjPB1L+ULX7vPf9xNlyVG+AAASBTRKIWvO3azA5jNFwjIepxNlwEko6j+kPaALoq+bXYQAIPN9ntJ15qdIm6lfOGTGMkDUoFLIT2U/nudFFlrdhQAsWCl0nSH747U5RpdiiCQHHIsrXrM8ytNiewwOwqAWLEyH787KV/4jnekGoDkMcpap8fcd6g4Wm52FACxZKHwdYfCJ3U5xEcVBBLbLPt+PeD4pXKjDWZHARBrzMfvFoWvuxE+TtoAEtZZjm36vf03SjfazY4CYCgwh69bfHcs4qQNIMlcEnlTv3A8JJfBTqxAyuCWbrdSvvB1N8LHLV0g8Xwr9KhuynhJNn4DA6mFRRvdovB1DPF18RyARPKz4O/0de8qs2MAMAMjfN2i8HXznNPK7SAgEdgU0e+CP9dFXrZdAVKW3Wl2griW8oVPFou6GuJzsmgDiHvp8umv4Z9qkbfC7CgAzOTJMTtBXEv5wtfdHD4XI3xAXBumBj0Y+YmmZTSZHQWA2dLyzE4Q11J+05qux/e4pQvEs/Eq1TP6gaalU/YASPLkmp0grlH4uhnhs1skG1u2AHFnnrFVT1pvVonbZ3YUAPHA7pGcaWaniGspX/i6HeITo3xAvDkv+k895LhDec6Q2VEAxIs0Rvd6kvKFz2G3K2p03fiYxwfEj6vDz+sP7j8p3c7vSwBH4HZuj1J+0UZ6mkfhcLjL51mpC8SHG4N/1b9mviMrG2QC+DxG+HqU8oUvIz1NkWjXowWM8AFmi+rO4F260rvR7CAA4hWFr0cpX/jcLhebLwNxyqWg/hS6VWd595sdBUA8Y0uWHqV84fO4XR3Hq3XBxS1dwBTZatUD4Z/oxMxas6MAiHfM4etRyi/acLucMrpZpssIHzD0SlStZ4wbdWIGZQ9ALzDC1yMKn9PZ7U57FD5gaE3XLj1t+ZHGe1rNjgIgUTCHr0cpf0vX5XLJarEoGo3Kaj22/3JLFxg6S6Jr9UfH3fI6ImZHAZBIKHw9SvkRPo/bJZvNpnD4+H/BuG38xQMMhS+G39RfXb+h7AHoO+bw9SjlR/jcLqfsNpvCkYicchzzfAaFD4i5b4Ue1U0ZL8nGHnsA+oMRvh6lfOFzuZyy2zsK3/FkUviAmPpZ8Hf6uneV2TEAJDIWbfQo5Quf2+WS3W7v8rQNp9WQyxJVwEj5u9/AoLIpot8Ff66LvDvMjgIgkdmckivT7BRxL+VbjNt56JZuF3P4JCnD3vXRawD6Ll0+PRz+EWUPwMAxf69XUr7wOZ0OOR32Lm/pSszjAwbTMDXoqciNWpRRYXYUAMkgu8TsBAkh5QufxWJRepqn2xE+5vEBg2O8SvWsfqBp6U1mRwGQLPImmp0gIaR84ZOkbG+mQqFQl88zwgcM3Dxjq56y/kQj3T6zowBIJvkTzE6QECh8kgoL8hQIBrt8PpM5fMCAnB/9UH9z3KFcJ7+XAAwyRvh6JeVX6UpSTpa32+cZ4QP67+rw87ot7Uk5+OclgFjIp/D1BoVPHbd0JckwDFksx+78yhw+oH9uDP5V/5r5jqxsqAwgFixWKXec2SkSAoVPUpY3s+N4tUhEDvux3xL24gP6Kqo7g3fpSu9Gs4MASGbZoyS7y+wUCYEGIykrM0Nul1OBQNfz+NiLD+gdl4L6v9DNlD0Ascf8vV6j8Knjlq7L6ZQ/EOjyGubxAT3LVqseD/9AZ2XuNzsKgFTA/L1eo/BJSvO4lZGeJn83I3zM4wO6N0pVeta4QSdm1JodBUCqyGNLlt6i8Klj8+XC/Nxub+lmc0sX6NJ07dJTlps0ztNmdhQAqYQRvl6j8B1SVJDf7V58uY6uN2YGUtmS6Fo9artdha6uf/8AQEwwh6/XWKV7SHZWZrfP5zpCssiQIfaXAA77YvgN3el5SC6bYXYUAKnGmSl5h5udImEwwnfIkXvxHY/dImVxWxfo9K3Qo/pV+oOUPQDmyBtvdoKEwgjfIdmH9+ILR+RwHP/bkucIqTHsGOJkQPz5WfB3+rp3ldkxAKQy5u/1CSN8h2R5M+V2db81Sy4jfEhxNkX0h+BtlD0A5mP+Xp9Q+A7J9mbK7XLJ383CjTwWbiCFpcunh8M/0kXeHWZHAQApny1Z+oLCd4jH7VJutlftPn+X11D4kKoKVa+nIzdqUUaF2VEAoAMjfH1C4TvEYrFo/OgStbf7urzGY4vKY2UDZqSW8SrVM/qhpqY3mR0FADpYHczh6yMK3xFGFBYo2sUq3cMY5UMqmWds1VPWn2iku+t/CAHAkBs+U3J4zE6RUCh8RygsyJPNYlE43PXiDAofUsX5kQ/1N8cdynWyWAlAnCk5yewECYfCd4TCgjylp6eprZvbupy4gVRwdfh53eO5V2n2qNlRAOBYoxaanSDhUPiOkJedpazMDLV2U/gY4UOyuzH4V/08/Uk5+NMBQLxihK/P+CP9CDabTWNLirsd4cuyRWS3MOqBZBTVncFf6N+878jKCYIA4lXOGCmz0OwUCYfC9zklxUUKdTOHz2JhA2YkH5eCuj94s670bjQ7CgB0j9G9fqHwfU5hfp4skqLRrkfxCp1db84MJJpsteqJ8A90pne/2VEAoGfM3+sXCt/nFBXkyeN2q93f9QbMRa6uj18DEskoVelZ4wbNzqg1OwoA9E4Jha8/KHyfMywvVxnpHrW1dT2Pr2OEr/v9+oB4N1279JTlJo3ztJkdBQB6x5UlFUw1O0VCovB9jsvlVHFRYbcLN9xWQznM40MCWxJdq0dtt6vQxfQEAAmkZL5kpbr0B9+14xg3qlj+QPe3bYuYx4cE9cXwG/qr6zfyOjgmEECCYcFGv1H4jqOoIF+GJKObY9aYx4dE9K3Qo/pV+oNy2ZiSACABsWCj3+xmB4hHhQV5cjkc8geC8rhdx72GET4kmluCv9O/eFeZHQMA+sdql4rnmZ0iYTHCdxzFRcPkzcxQc0trl9ek26Ly2pjHh/hnU0R/CN5G2QOQ2IpmSM40s1MkLArfcaR53Bo/eqSaW7sufJI0gtu6iHPp8unh8I90kXeH2VEAYGCYvzcgFL4uTBw7WsFg9+fmFlP4EMcKVa+nIzdqUUaF2VEAYOCYvzcgFL4ujCoukt1uVzDUdekb7gqI/fgQj8arVM/oh5qa3mR2FAAYHIzwDQiFrwujRhQpKzNDTd3M43NbDeU5uh8FBIbaPGOrnrL+RCPdXe8lCQAJJXe85B1udoqERuHrgjczQyUjirpduCFJI1itizhyfuRD/c1xh3KdLCgCkESmXGB2goRH4evGlAlj5fd3P0+PhRuIF1eHn9c9nnuVZo+aHQUABtfkC81OkPDYh68bo4uHy2azKRQOy2E//rdquDMomyWqiEF3hnluDN6nf818V1aL2UkAYJClF0glLNgYKFpKN8aOKlaWN0ONzS1dXmO3GhrJKB9ME9Uvg3fq37yUPQBJatL5nJ87CPgOdiMrM0NjS4rV1NR14ZOk0W7/ECUCPuNSUPcHb9YV3k1mRwGA2JnC7dzBQOHrwbSJ4xUIdL8wY5TbLwvbs2AIZatVT4R/oDO9+82OAgCx40iXxp1hdoqkQOHrwdiSEXI4HQoEuy59bquh4azWxRAZpSo9a9yg2Rm1ZkcBgNgaf4bkcJudIilQ+HoweuQI5WRlqrGn27oe9jxD7E3XLj1luUnjPG1mRwGA2JtykdkJkgaFrwdpHrcmjxvT7cINSRrj9otTNxBLp0bX6FHb7Sp0MZoMIAVYbNKk88xOkTQofL0wZcJYhcNhGUbXhS7dFlUBp24gRr4Ufl33uX4rryNidhQAGBqjT5bScs1OkTQofL0wadxoZWakd3vMmsRqXcTGt0OP6K70h+SyMYIMIIWwOndQUfh6obhomEYXD1ddQ2O3142h8GGQ3Rr8nX6c+bJs7LEHINVM5ji1wUTh6wWLxaI5M6bK5wt0e1s32xFWlp3buhg4myL6Q/A2Xe9dZXYUABh6hTOknNFmp0gqFL5emjJ+rNLT3Gpr7341LqN8GKh0+fRw+Ee6yLvD7CgAYI4pjO4NNgpfL40qLlJxUaFq6xu7vY7Ch4EoVL2ejtyoRRkVZkcBAPMwf2/QUfh6yWazae6MqWptb+/2unxHSOlWVlKi78arVM/oh5qa3mR2FAAwT1aJNHyW2SmSDoWvDyaPHyO306l2X9ejeBaLNIZNmNFHC4wtesr6E41082sHQIpjsUZMUPj6YPzoEhUW5PW4WndSWvejgMCRzo98qAcddyrXGTY7CgCYb+YVZidIShS+PnA47JozfYqae9iPL88RVp6D0xDQs2vCz+kez71Ks0fNjgIA5iucLo2cZ3aKpETh66OpE8fJZrMpEOy+0E1mlA89uDF4n25Pf0oOfhcCQIc515qdIGnxV00fTRw7SsPyclXX0P3E+vEen2ycrYvjiuqXwTv1b953ZWVDZQDoYPdwOzeGKHx95HG7NfuEyWpsaun2OpfVYPEGjuFSUPcHb9YV3k1mRwGA+DLtYsmTbXaKpEXh64epE8fJYpFC4e4n2bN4A0fKVqueCP9AZ3r3mx0FAOLP3OvMTpDUKHz9MHncaOXnZquuh02YRziDyrCx8hLSKFXpWeMGzc6oNTsKAMSf/MnS6EVmp0hqFL5+8GZmaP6s6aprbOz2OouFUT5I041detpyk8Z52syOAgDxac7XzE6Q9Ch8/TRnxlS5HE61+bqfpzfJ45OFxRsp69ToGj1qv13DXGzTAwDHZXNKs64yO0XSo/D106SxozSmZISqauq6vS7DHtEIV2CIUiGefCn8uu5z/VZeB0ftAUCXplwkpeeZnSLpUfj6yW636+R5s9Xe7lM02v2mudzWTT3fDj2iu9IfksvG6C4AdIvFGkOCwjcAs6ZNUk6WV/WNzd1eN8btl8vCSQqp4tbg7/TjzJdlY489AOhezlhp7Klmp0gJFL4BKMzP08xpk1RTV9/tdTaLNJ5RvqRnU0R/CN6m672rzI4CAIlhztc6Vjgi5ih8AzR/1nRZLZYej1qbQuFLauny6eHwj3SRd4fZUQAgMVgd0olfNTtFyqDwDdD0yeM1vLBA1bXdj/LlOsIqdvmHKBWGUqHq9XTkRi3KqDA7CgAkjsnnSxnDzE6RMih8A+R2uXTy3Flqam6VYXQ/QX9mRusQpcJQmaiDekY/1NT07s9WBgB8zpzrzE6QUih8g2D2CVOUkZ6m5tbuN9YtdgWVZw8NUSrE2gJji56w3qyRbs5MBoA+yRoljT/T7BQphcI3CEaPHK7J40f3uCefJM1glC8pnB/5UA867lSuk6PzAKDP5n9dslJBhhLf7UFgsVh00okzFQ6HFY50v8nuOI+P83UT3DWh53SP516l2dlqBwD6zJMjzf+G2SlSDoVvkMycNkkFeTmqrWvo9jqrRTohnTNVE9WNwft0e8ZTcvA7BwD6Z+F3JVeG2SlSDn9tDZKszAwtmD1dtfWNPS7emJzWLicbMSeYqH4ZvFP/5n1XVraMAoD+cXmlhd82O0VKovANopPnzlZmRpoam1u6vc5pNTSFUb6E4VZA9wdv1hXeTWZHAYDENv8bkifb7BQpicI3iMaUjNCJ06eqoqq2x2tPSG+TVZyzGu9y1KLHwz/Qmd79ZkcBgMTmSJMW/avZKVIWhW8QWSwWnbpwjpwOu1rbuj9ZI90W1XgP23nEs1Gq0jPGjZqd0fPqawBAD+b9i5SeZ3aKlEXhG2RTJ4zV1IljVVZZ3eO1HRsxM8oXj6Ybu/S05SaN83DrHQAGzO6WTv6+2SlSGoVvkNlsNp2xaL4Mw5A/0P35ujmOsEa6AkOUDL11anSNHrXfrmGu7v//AQB66cSvSplFZqdIaRS+GJg1bbLGlhSrvKq3o3yIF18Kv677XL+V19H9fooAgF6yOqTF/2F2ipRH4YsBl8upM06eL5/Pr3C4+02WR7iCKnIyyhcPvh16RHelPySXjdvsADBoZn1Zyi4xO0XKo/DFyPxZJ6i4aJgqqntesTvf2zwEidCd24K/048zX5aNPfYAYPBYbNKS/zQ7BUThi5nMjHSddtI8NTW3KhrtfpPlQmdIo9ys2DWDXWH9IXibrvOuMjsKACSf6ZdKuePMTgFR+GLqpDkzlZebpZoejluTpHmZLbKwYndIpcunh0M/0kXeHWZHAYAkZJFO/YHZIXAIhS+GCvJytHjebNXU1fd43FquI8y+fEOoUPV6OnKjTsqsNDsKACSnqcukgslmp8AhFL4YWzxvtryZGWpo6nme3tzMFk7fGAITdVDP6Ieamt5kdhQASF6n/tDsBDgChS/GRhUP15zpU1RRXdvjKF+mPcIZuzG2wNiiJ6w3ayRzJgEgdiadLw2faXYKHIHCF2MWi0VnLl6odI9Hjc0tPV5/YkarHJbuF3mgf86PfKgHHXcq19n9VjkAgAGw2qWzbzM7BT6HwjcEJo4dpYUnTld5ZXWPo3weW1TTGeUbdNeEntM9nnuVZqdMA0BMzfu6NGyq2SnwORS+IWCxWHTeaScry5up2vrGHq+fkdEqt5WTHgbLfwbv0+0ZT8nBr3YAiK20POmMH5udAsfBX4FDZFTxcC1ZcKIqa2p73JfPaTU0iyPXBkFUvwzeoe9735WVDZUBIPbOuFny5JidAsdB4RtCZ51ykgpyc1RVW9/jtVPT25RuY65Zf7kV0AOhn+gK72azowBAaiicIc293uwU6AKFbwgVFeTpzMULVFvfoEik+1E+u0Wawyhfv+SoRY+Hf6AzMg+YHQUAUsfSX0hWakW84v/MEDt90TyNKCxQRXVNj9dOTGtXniM4BKmSxyhV6RnjRs3OqDM7CgCkjhO+KI1ZbHYKdIPCN8Rys7N07pJFamxqUTjc/S1bq0VanNXEkWu9NN3YpactN2mch1XOADBk7B7pnJ+bnQI9oPCZ4JQFJ2r0yOEqraju8dphzpAmp7UPQarEdmp0jR61365hLkZEAWBInfIfUnaJ2SnQAwqfCTIz0nXeaSer3edTMBjq8fr53ma2aenGl8Kv6z7Xb+V18D0CgCGVVSIt/nezU6AXKHwmWTR3piaMKdGB8soer3VZDS3w9nwWbyr6TvAR3ZX+kFw2bnsDwJA79+eSw2N2CvQChc8kHrdb55++WOFwWD5/oMfrJ3p8KnL2fF0quS14t/7L+7Js7LEHAENvzJKOxRpICBQ+E82fNV3TJo7TgbKKHq+1sICjk11h/SF4m67zrjY7CgCkJoutYxsWJAwKn4kcDrsuOHOJ7DabGptberw+xxFO+XN20+XTw6Ef6SLvDrOjAEDqmne9VHiC2SnQBxQ+k82aNkmL581WaXllj0euSdKczJaUPYGjUPV6OnKjTsrsed4jACBGPDkdR6ghoVD4TGaxWLTs3NNUNKxAZZU9b9PisBo6KQUXcEzUQT2jH2pqepPZUQAgtZ1xs5SWa3YK9BGFLw4U5ufpwrOWqKW1Tf5Azwszxnr8KnH5hyBZfFhgbNET1ps10u0zOwowKO79OKiZ97bKe0ezvHc0a9H/tenVnZ9t0VTZGtU1z/lU9KsWpf9Ps+b8b6ue2dbzFk6H3fnPgCy3N+s/Xjv6z4kbX/cr9xfNKvltix7ZdPT7PbU1pGWPsecnejByvjTv62anQD9Q+OLEqQvmaMaUidp7oKxX1y/KapLN0vMt4ER3fuRDPei4U7nO1LyNjeQ00mvRnWe7tPZb6VrzrXSdOcamix/3aWt1x16SX3vOp09rI/rHVWna/N0MfWmqQ1c87dP6ip73mvy4LKL/XRvUzMKj/3h/8dOQHt0c0hvXpOuXZ7v1jRd9qm3v+DOkyW/o5ncC+uMF7sH/YpE87G7pkns5LzdB8X8tTrhcTl1y3hnyuN2qa2js8XqvPaLZGa2xD2aia0LP6R7PvUqzJ3+xRWpZNtmhCyY6NDHPpkl5Nv33WW5lOKWVpR2FbvnBiL6/wKkFxTaNy7Hqp6e6lO22aG0Pha81aOjqZ326b5lHOe6j9yvaXhvV6WNsmjfCpqtmOOR1WbS3oWPV/4/e9Ou78xwalcVfCejGmT+V8ieanQL9xO/uODJlwlidftI8VVTVKBLpueTMymhVviM5jxL7z+B9uj3jKTn4FYokF4kaenxLSG0haVGJTZJ0colNT2wNq95nKGp0PO8PGzp9jL3b9/reK35dONGus8cde92sQpvWlEfU4DO0tjwiX8jQhFyr/nkgrHWVEf3bQmdMvj4kiZKF0knfMzsFBqD7Pz0wpCwWiy446xRt3L5DB8orNLakuNvrrRbp9JwGPV9ToLCRLM0oql8Gf6ErvJvNDgLE1OaqiBb9X5v8YSnDKT33ZY+mFXQUvicvT9OXn25X3i9bZLdKaQ7puS+naUJu17/PH98S0rqKiD7+Zvpxnz9vgl1fnenQ/Pta5XFY9NAlHqU7pe++7NeDF3t075qQ7lkdVH6aRX+5yK0Thtli8nUjAdk93MpNAvzfizO52Vlads6pCgQCavf1vDAj2x5JmmPX3ArogdBPKHtICZPzrdrwnQyt+ka6vjvPqWuf92tbTcct25+941ej39Bb16RpzTfTdeNJLl3xVLs2Vx3/lu7Bpqj+/TW/HvmSR25710fP3Ha6W7v+LVObv5uhL0516I4Pgzp7rF0Om/T/fRDQP69P0zdOdOhrz7NACkc462dS3nizU2CALIZhcHRDnAmHw/rd/Y9q7aZtmjpxnCyWns8Oe60uV6WBxJ1wnaMWPRD+iWZn1JkdBTDF2X9r0/gcq3602KUJ97Rqy3fTjxplO/tvbZqQa9WfLzr23NLnPwnpi0/4jjpmMGJIFnXcCQj8NFM269F/jnxSG9Gyx3xa/+103b8+qH8eiOjJy9PUFjSUcUeLmv8rU5kuzi1MeaNOlq57mdG9JMAt3Thkt9v1xfPO1M49+1VT16Bh+T3vd3RqdqOeqSlQIJp4t2FGqUoPGj/VuIzUPkUEqS1qSIGI1B7q+Df45/qZbNaOa47nrLF2bf7u0bdyr3/Bpyn5Nt202HlM2TMMQ99+ya/fnOtShtOiSFQKHZo2fPi/EYYC4EiTLvkjZS9J8H8xTo0bPVJnLzlJ1bV1Cod73pIkzRbVKVmJtynxTGOXnrbcpHEeyh5Sx4/f8uuD/WHta4xqc1VEP37Lr/f2RXT1DIem5Fs1Ideqb7/k1+qyiHbXR/Xr5QG9uTuiS6Z89m/0s/7Wpj+s7li0lemyaPow21E/0h0W5Xk6Hv+8v64LqSDNomWTHZKkxaPsemdvWCtLw/rtioCmFViV7WZ0L+Wd9z9S7jizU2CQMMIXx849dZHWbdmuvQfLNXHsqB6vH+vxa6K/XTt9aUOQbuBOi36sexy/l9fR895iQDKpbjP0ted8qmg1lOWyaGahVa9/NU3njO/4I/mVr3j0X28HtOyxdrUGO1bTPnSJWxdMdHS+x+76aOc+en1R1RrVf38Y0PKvfzYiuKDYpv9c5NKFj/o0LL1jQQdS3OQLO87LRdJgDl+cW7dlu/7wwOPK8mYoLye7x+uDUYuerSlQayS+u/ylkdf1P+6/yWXjlx8AxBMjo1CW766Q0vPMjoJBxC3dOHfiCVN0zqknqaKqRsFQz0crOa2GTstulEXxW6S+E3xEv0x7iLIHAHHIcvEfKXtJiMIX5ywWi5adfZqmTRqv3fsOqjcDssNdQU1Pj885cbcF79Z/eV8+ajUhACBOLPiWNPEcs1MgBih8CSAjPU1XfuF8Zaanqaqmd9uWzPM2K9fe+8PWY82usP4QvE3XeVebHQUAcDwFU6Rzfm52CsQIhS9BTBo3WhedfarqGpvk8/e8IbPt0Ckctji4tZsunx4O/UgXeXeYHQUAcByGzSld+lfJkbj7uaJ7FL4Ecs6SRZo7fap27y/t1a3dXEdYi0zeqqVQ9XomeoNOyqw0NQcAoGuWc/6fVDTD7BiIIQpfAnG5nLry4vM1LC9HB8t7V6CmpLdrcpo58/km6qCe1Q80JS05jn4DgKQ06yrppO+anQIxRuFLMCOHF+pLS89Wu8+vltbeFbmTs5pU4AjGONnRFhpb9KT1ZhW7e779DAAwhzH8ROmiu82OgSFA4UtASxacqMXzZmtfabkikZ43XrVZpLNz6+W2Ds0Gx+dHPtQDjjuV4+z5hBAAgDminnxZrnyEeXspgsKXgGw2my678ByNLh6ufaVlvXpNui2qM3MaYr4/3zWh53SP516l2ft+AgAAYGgYFrusVz4sZRWbHQVDhMKXoArycnTZhefIMAzVN/ZuYcYIV1ALvLGbT/efwft0e8ZTcvCrCgDimrH0F9LoRWbHwBDir+YENn/WCTpr8UKVVVYrGOzdnnszMto03tM+yEmi+mXwDn3f+66sbKgMAHEtNPMrsi74htkxMMQofAnMYrHokvPO0IwpE7Vz7wFFo727jbokq2nQNmV2K6AHQj/RFd7Ng/J+AIDY8RfMlOMLvzM7BkxA4UtwmRnpuvayZSoalqe9B3s3n89uNXR2br1cloHNs8tRix4P/0BnZB4Y0PsAAGIv6MqV+2tPS3an2VFgAgpfEigZUaSvXHKBrBaLqmvre/Uarz2i0wewiGOUqvSscaNmZ/TuqDcAgHkiFrvsX31Kyiw0OwpMQuFLEvNmTtOyc05TbX2DWtt6N0evxB3QnMyWPn+umcYuPW25SWM95mzoDADom+jSX8taMs/sGDARhS9JWCwWXXjmqVo8/0TtO1CmULh3e+DNzmjVaLev15/ntOjHeth+u4a5hnYjZwBA//hnfk2OBdeZHQMmo/AlEYfDrq9cslRTJo7Vzj37e3XersUinZHdqPxenMRxaeR1/cV1t7yOodnAGQAwMP7CuXJf/FuzYyAOUPiSTE6WV1+7dJnyc7O1v7SiV6+xWw2dm1uvTFvXo4LfCT6iX6Y9JJctths3AwAGR8AzTO6vPSXZ7GZHQRyg8CWhcaNH6sovnK9wOKza+sZevSbNFtV5eXXHXbl7W/Bu/Zf3ZdnYYw8AEkLE6pTjmqel9DyzoyBOUPiS1KK5s3TBmaeoqqZW7T5/r16TbY/onNx62Q6t3LUrrD8Gb9V13tWxjAoAGEQR2RS9/O+yjphldhTEEQpfkrJYLLr43DO08MQZ2rP/oMKR3s27K3IFdVpOgzLUrofDP9KF3p0xTgoAGCxRWeS/4HdyTD3f7CiIMxS+JOZyOfXVL12kCWNGadfeA71axCFJ2S17dF3Z/+ikjMoYJwQADBbDkBpP/qnSF1xjdhTEIQpfksvPzdY1l16krMwM7S/reRFHVeke7Vv1mnY0p2mDfc4QJAQADIbKad9U7rk/MDsG4hSFLwVMHj9GV3/xAkUjUVXW1HZ53cGdW1S+7m0Nz3Jq8alnaFvWGdrlmjGESQEA/bFvxEUquuIus2MgjrFWO0UsmjtLjc0teuz5V+V0OJSbndX5XDQa1d4ta9Syb51KhuVp3sKTZbV2/Fvg4/Sz5TT8GhVkLh8AxKNdmYs0+rr7ZbGwlQK6xghfirBYLDr/9MVaeuYpqqiq6Tx+LRqN6NM176tt31pNGFWs+UeUPUkyLFYtz7hAlfYSs6IDALqw0zFNhV9/VA6ny+woiHMWo7cz+ZEUgsGQ7n/iOb27Yo3GlYzQ7rXvy9q4T1MmTdKkqSd0+Tq7EdTZTU8oN1I9hGkBAF3ZYx2r9OufVWHJOLOjIAFQ+FJQa1u77v37k/rwxUeVEazVzBkzNWpsz39guKLtOqfpcXmjDUOQEgDQlQMqlu2ap1U8fprZUZAguKWbgjLS03T9FZdo9oQRGjeqWCNHje7V6wLWNL3jvUwt1uzYBgQAdKncKJBx+d8oe+gTRvhSWG1lqV548PeqKT+gkeOnHDV3rzueaKvObH5KWZH6GCcEABypOpqjli/+TeNPPNXsKEgwjPClsPyikVp61beUW1Ckin07e70xs8+aobe8X1aDrSDGCQEAh9VFM9Vw/p8oe+gXCl+KGzF6gs778jeUlpmlygN7el36AtY0veW9QrX2ohgnBAA0RdNUecZvNfnkC8yOggRF4YNGT5qucy67TnaHQzUVB3v9upDVrXe8l6vaXhzDdACQ2hqjHu1f9D+advplZkdBAqPwQZI0aeYCnXnJNYqEQqqv7vkItsPCFqfe9V6qCseoGKYDgNRUE8nQznn/rRnnX8fGyhgQCh86TV9wqk698Aq1tzarqa73++1FLA69n/lFlTnYCwoABsvBULY+nXO75l50PWUPA0bhQyeLxaK5py3V4vMuVUtjvRprq3r92qjFrg8yv6ADzkkxTAgAqWFXsEA7Zt2sRcuu6/UOCkB32JYFx4hGo1r9zov68JWn5EnPVO6w4b1+rcWI6qTW1zU2uC2GCQEgeW32D1fpjO/r7Mu/KYfTaXYcJAkKH47LMAytef9VffDi43K43covGtmXF2t+21uaGNgUu4AAkIRWtY9S1dRv6LyrviWX22N2HCQRCh+6ZBiGNix/W+8+/7BsdrsKRvRtYcactnc1xb8uRukAILm80zpBDZOv1AVf+a486Rlmx0GSofChW4ZhaMvqD/T2sw/JkDSseHSfJg/Pav9QJ/hWxy4gACS4qCx6tWWq2iZ8QRdc/V1leLPNjoQkROFDr2xb+5HeeuZBhUJBFZWM61PpO6F9pWb5PophOgBITGHZ9HzTdNlnfFHnXP4vSs/MMjsSkhSFD722Y9Nqvf7k/ynga9fw0RP6VPrG+zdpXtvbsikaw4QAkDiCcuipplnKmX+5zvri1+TypJkdCUmMwoc+2b11nV578q9qb27SiLGT+lT6CkKlWtLyD7kNXwwTAkD8a5dbjzfP1eglV+nUC7/MalzEHIUPfbbv08167fG/qLmhTiPGTurTHlHpkSad2vKCciI1MUwIAPGryUjXk60LNPWca7TonEtks9nMjoQUQOFDvxzcvV2vPvq/qq+t1MhxU/pU+uxGUItaXlVJaFcMEwJA/KmJZulZ/8mae+G1mnvq+ZyggSFD4UO/le/bqVce/bNqK8s0ctxkWfvyr1TD0EzfR5ruWxW7gAAQRz4NjdDb0YU69Utf1wnzTqHsYUhR+DAglQf36uVH7lV12T6NHD9FNpu9T68fHfhEC1tfl13hGCUEAHNFZdH7vina7p6rcy67XhNnzDM7ElIQhQ8DVlN+QC8/eq8q9u3SiLGT+zz5ODdcqVNbXlBatDVGCQHAHH6LR882TVdr7gydf+U3NWrCNLMjIUVR+DAo6qrK9fqTf9W+TzapsGRcn3eJd0dbdWrLP5QfrohRQgAYWjW2Qj1RM1npI6dp6VXfVlHJOLMjIYVR+DBo2lub9c5zf9fm1e8rO79QWbkFfXq91QhrYesbGhvcHqOEADA0PnWcoOfKizRy0gwtvfLbyiscYXYkpDgKHwZVOBTSyrde0Mq3XpDd4VTBiFF9npg81bdas9r/Kav4pQkgsYRl10rnKfqgzKEJ0+fq/Cu/JW9OntmxAAofBp9hGNq65p9674VH1N7arOFjJvZ5n6kRwd1a3PqKHEYwRikBYHC1Wr16w3K6Pq3ya+rcxTr38n9RWobX7FiAJAofYujg7u168+kHVXlwj0aMmSiny92n12eFa3Vqy/PKjDbFKCEADI4K+yj9o222mvxRzTnlXC258Aq53B6zYwGdKHyIqYaaSr31zIPauXmNCopH9/lgcHs0oPltbzOvD0Dc2uKap1cqC+TJzNKpF35ZMxae3qfN6IGhQOFDzPnb2/Tei49qw/K3lZmVq5yCoj6/x6jAJ1rQ9pacRiAGCQGg70IWpz5wnKGVZVEVj52os794rUaOn2J2LOC4KHwYEpFIRB+/97KWv/6sDMNQUcm4Pi/mSIs066TW11QUPhijlADQO022XL0SWaJ9dT5NPfFknXnJNfLm5psdC+gShQ9DxjAMfbpxld55/mE119eqeOwk2ex9O5lDhqGp/jWa2f6RbIrEJigAdGO38wS90jBOQcOqBWcu00lnX9znDeeBoUbhw5CrOLBbbz51v0r37tDw0RP6NbE5O1ytk1tfUXakLgYJAeBY7ZYMrXCfro9Lw8rKH6bTl12lKScu4kxcJAQKH0zRXF+rt559UJ9sWKX8opHKyMrp83vYjJBmt3+gyf4Ngx8QAI6w1zlVH2q+SssqNHrSdJ31pWtVVDLW7FhAr1H4YJqA36cPX35S6z58XU63p1+bNEvS8OBendT6ujxGWwxSAkhlPkuaVmeco82N6WptqteMhafr9C98pc87DgBmo/DBVNFoVJtXva+PXn9aTXU1Gj5qvJz9uMXrirZrYesbGhnaHYOUAFLRPucUrfacrgOlZbLbnTr5vC9p7qnn933uMRAHKHyIC9Vl+/X+i49p55a1ysorUHZeYb9G+8b7N2lu27uyKxyDlABSgc+Spo/Tz9Yelah8304VDC/RmV+8RuOnnWh2NKDfKHyIG8GAXx+/+4o+fu9lBf0+FY0eL7vd0ef3yYzU6+SWV5UXqYxBSgDJbL9zktakn6XaFr/qKko1YfocnfWl65RXOMLsaMCAUPgQd/bv2KL3X3xMpXs+Vf6IEmV4+76gw2JENd23QtN8H7N9C4Ae+S0erUk/S3vt41V1cK8kafbJZ2vJBZfL5UkzOR0wcBQ+xKW2lib989WntXnle7LabBo2cky/jirKjDRobts7GhHaN/ghASSFg84JWp1+jupaA6otP6iiUeO15ILLNWH6XLZcQdKg8CFuGYah7euW65+vPKXayjIVjR4ntye9X+81MrBTc9rfU0a0eZBTAkhUAYtba9LP1B77JFWX7pVhGJqx8HQtPu9L/doqCohnFD7EvfrqCr3/0uP6dMNKpXuzlTtsRL/+1W0zQjrBt0pTfWu4zQukuAPOiR1z9VpDqik/oKKSsVpyweWaOGM+o3pIShQ+JIRwKKT1H72plW8+r7aWJg0fPbHfRxllRBo0r+1djQjtHeSUAOJdva1A69JPV4W1WFWle2VEo5qx8DQtPu9SZWbnmh0PiBkKHxJK+b6deu/Fx7Tvk03KLRwhb07/DysfGdypOW3c5gVSgc+Sro1pi7XHNV1trc2qKduvYSPH6JSll2nyrIWM6iHpUfiQcPztbVr51gta9+HrikYjKiwZL5vN1q/3shkhTfOtZjUvkKTCsusTz1xt8yxQMGpTVek+RSMRTV94qhafd6m8OXlmRwSGBIUPCckwDO3euk7vv/SEqkr3KDu/SFm5Bf3+V3pGpFFz295RMbd5gaSx3zlZ69NOVbvNq7aWpo5RveLRWnz+pZo8+6R+rfwHEhWFDwmttblRa95/VRuXv6321hYVlozp90peSSoO7tLctveUEW0axJQAhlKtvUjr0k5XraNY0WhU1aX7FA6HdMK8U7Rk6eXy5vZ/KgiQqCh8SArl+3dpxRvPadeWdbI7nRpWPFo2W//Ou7Qa4c7VvBzRBiSONmuGNqadqn3OKZLFovbWZlWX7Vd+0Uidcv6lmjLnZEb1kLIofEgakXBY29ev0Kq3/6Gqg3uVnV+orLxh/b7NmxZp1gm+VRoX2Mr8PiCOheTQds98bffMU8TiUCgYUHXZfsmQps49WacsvUzZecPMjgmYisKHpNPa3Ki1H7ymDR+9pfbWZhWOHCt3Wv9v835W/LbIpuggJgUwEIakva4TtDHtFPmsGYpGIqqtLJWvrUUl46ZowVkXacL0eYzqAaLwIYmV79+llW8+r52b18rucGrYyP7f5pUofkA8qbKP1Pr001RvL5JhGGqqq1ZjbZXyi0Zq3ukX6IT5S+Rye8yOCcQNCh+SWiQc1icbVmrlWy+oqnSvsvMGdptX6ih+030rNTawleIHDLEyx1ht8yxQjWOkJKn90J566d5szVx0puacci5brQDHQeFDSmhradLa91/T+o/ePHSbd4zcaRkDek+KHzA0orLogHOStnkWqNHeMRcvGPCrunSfrFarJs1aqAVnXqSikrEmJwXiF4UPKaXiwG6tePN57dq8Vja7XcOKx8hm7/9tXklKjzTpBN8qih8wyCKyaY/rBG33zFerLbvjsUhEtRUHFWhv1ahJ07XwzIs0dups5ukBPaDwIeVEIhF9un6FVr79D1Ue2KPMnDzlFAwf8F8YFD9gcIQsTu10zdInnrnyWzsWXBmGocbaKjXVVWtY8WjNP+NCTZu7WA6ny+S0QGKg8CFltbc2a/0/39LGlW+rsaZKmTn5yikoGqTit1LjAttkpfgBvea3ePSpe452uGcrZHV3Pt7W3KjaioPKyMrR7MXn6MRTzlGGN9u8oEACovAh5TXX12rz6g9iUvwm+9dpXGCrnEZgkNICyafV6tUn7rna7Z6hiMXR+XjQ71NV6T7ZHQ5NOXGRFpxxoQpGjDIxKZC4KHzAIbEqfjYjpNGBTzTRv1F5kapBSgskvkZbnrZ75mufc6oMy2e/zwK+dtVWlioajWj0xOlaeNYyjZk8Y0Cr64FUR+EDPidWxU+ScsOVmujfqNGBTzi2DSmrxj5C2zzzVeYYLx1R4nytLaqtLJXVatXI8VM0a9GZmjhjvhxOp4lpgeRA4QO60Fxfq02r39emle+osbZK3ux8ZQ9S8XNE/RoX2KYJgY3KitQPQlogvgUsbu1zTdUu10w12fM7HzcMQ20tjaqvKpfD4dKYKTM0a9GZGjtl1oBX0AP4DIUP6EFTfY02r/5Am1YcKn45g1f8JGlY6IAm+jdqZHAXq3uRdKrtxdrlnqkDzkmKWj4rcIZhqKWxTvXVFfKkZWj8CXM0a9EZKpkwjS1WgBig8AG9FOvi5462abx/syYENik92jIo7wmYwW9xa6/rBO12z1Cz7ehTL6LRqJrqqtVUX6P0zCxNmX2SZiw8TcNHT2COHhBDFD6gj5rqa7R51fvatPJdNdZWKTMnT9n5hQM6p/dIFiOqEaG9mujfqKLQPlnFb1HEv6isqnCM0V7XNJU6xx81midJ0UhEDTWVammskzc3XyfMPUXTF5zKqltgiFD4gH46XPw2r35fjbVVsjucyiscMeAj246UHmnSuMBWlQR3KDtSN2jvCwyWetsw7XVN0z7XVAWsacc8Hw6HVF9VrvaWJuUMG64ZC0/XCfNOUU5+oQlpgdRF4QMGqK2lSbu3rtfWNR+qfO9OBYN+ZeUWyJtbMKhzkTIj9SoJ7NSo4A7lRqoH7X2Bvmq3ZGifa4r2uk44agHGkULBgOoqyxTwt6lg+CjNOvksTT1xkTKycoY4LQCJwgcMmkgkorK9n+rTDau0Y+NqNTfUyuVJV86w4XK5PYP6udIjTSoJ7lRJcIfywxVi5hNirc2aoXLHOB10TlSlY/RR26kcZhiGWpsb1FhTJclQ4cixmr34bE2etVCe9MEb+QbQdxQ+IAaa62u1a+s6bfn4A1Ue3KNoJKrs/GHKzM4b9InpnkiLSoK7VBLcqYJwKXP+MCgMSXX24SpzjFOZc5wa7cO6vDYUDKihplLtLU1K92Zr9MTpmjp3kcZOmSWny93l6wAMHQofEEPhUEgHdm3V9nUrtHvrOrU2NSgtM0s5BUUxOfTdFW3XyEPlrzB0gG1e0Cchi1MVjtEqP1Tyjjcn7zDDMNTaVK+G2ipZZFF+0UhNm7dYE6bPVX7RSFbcAnGGwgcMkbqqcu3cskZbP/5QtRWlkqScYUVKz8yOyV+OjqhfI0O7VRLYqaLQfk72wHG1WLNU7hynMsc4VTtKFLXYur0+4PepsbZKvrZmpWdma+yUWZp64kkaPXkGo3lAHKPwAUMsGPBr7ycbtW3tcu3fsUXtrU3K8ObKm5sfs78w7UZQhaGDh34cUHakhnl/KSoqi2rtI1TmHKcyx3g12/N6fE04HFJzXY1amupldzhVMLxE0+Yu1vgTTlTusBGM5gEJgMIHmMQwDFWX7dfOzWu0fe1HaqirUiQUVpo3S96c/EFf6HEkZ9T3WQEMH+B4tyTXZs1Ujb1YZc5xqnCMUdDa86+taDSq1qZ6NdXVyDCiys4bpkmzFmj8tBNVPHay7A7HECQHMFgofEAcCAb8Kt+3Uwd2btPOzWvUUFOhUDAoT0amvLn5cnvSY/r53dE2FYYOqiBUqoJwubIitSz+SFAhOVRvL1Sdfbhq7cNV6xguv7V3K2QNw1B7S5OaGmoV8vuUnpWjMZNmaNLMeRo9aQYrbYEERuED4kwoGFTFgV2Hyt/Hqq+qUCDQLk96prw5+XKnZcT8Fpo9GlB+uEIF4TLlh8uVH6qQQ6GYfk70nSGp2ZarWvvwzoLXZMuXYen9/o/hcEitjfVqbWpQJBKWJz1Tw4pHa+qJizRm8gzlFBTF7gsAMGQofEAci4TDqjiwWwd3b9eOTR+rrrJM/vZWudLSlZVbIE965pDMn7IYUWVHalQQKldOpEpZkTplRerlMIIx/9z4jN/iVt0R5a7OPlwha99WexuGoaDfp+bGOrW3NMlqtSkjK0ejJkzT6EnTVTx2IvPygCRE4QMSRCQSUXXZPh3ctV07N69Rddl++dtb5XB7lJWTr7TMrCH/S9oTaTlU/jp+eA8VQZfhH9IcySYqi3zWDLVYc9Rkz+0seK22/p1SEY1G1d7SpJbGOgX8PrlcHmXlD9P4aSeqZPwUjRgzUWkZ3kH+KgDEEwofkICi0ahqyg/o4O6O8ldVuk/trc2yO5zK8GYr3Zsdk33+essdbesogeHDJbDjh9vwmZYp3kRkVZs1S622bLXYstRqzen4ry1brdYsRS32Ab1/OBRUy6FbtdFoRGkZXhWOHKPx007UiDETVThyLAsvgBRC4QMSnGEYqq0sVenuT7R/x1aV79+p1uZGRUJB2Z2uzgJodzjNjipn1NdZ/jIjDfJE2+Qy2uWOdvxwGb6kWiwSll2ttiy1WLMPFbtstVqz1WLLUbs1s09z7XpiGIb87W1qaayTr7VFNrtNmdl5Gj1pukZNPEHFYyYqO7+QW7VAiqLwAUnG396m6rJ9qirdpwO7tqny4B61NjcqGonI6XIrLTNL6ZlZcVEAj2EYchm+Q+XvsyLo/tzHrkMfD9VCkqisClpcCllcClpcClrd3fy84+N2a6Z81ozjnjk7GMKhoNpbW+Rra1agvV2GDLk8acrJL9T4E+Zq5LhJGj5qAitrAUii8AFJr721WVWlhwrgzq2qLt+v9tZmRcNhWW12pWVmKS3DK5cnLeFGf2xGSO5ou2xGWBZFZZEhiwxZjcMff/aYxTji48OPG0f/XNJnJe5QeQtZXQpbzC3H0WhU/vZW+Vpb5GtrUSQSls1mV1pGprLzizRy/BQVDC9RXmGxCoaXyGYf2O1gAMmHwgekmPbWZtWUH1BtRakqDuxW+b4dam1uUsDXLovVKndaujwZmXJ70mV3OBOuBCY6wzAUCvjV3tYiX2uzgn6/ZJE8aRlK92areOwkFY4cq/yiYuUVjVS6CYt1ACQeCh+Q4kLBoOqqylRbcVBVZftVunu7mhpqFfC1KxwKyiKLbA67XO50uTxpcnnS5HC6KBmDwDAMhUNBBXztam9rkb+tRUY0KofLrbSMTOUPH6XiMROVP3yk8otGKjuvMKlG7+69917de++92rdvnyTphBNO0C233KKlS5cedZ1hGLrgggv02muv6bnnntMll1wiSdq4caPuvPNO/fOf/1Rtba3GjBmj73znO/r3f//3If5KgPiXPH9yAOgXh9OpopKxKioZq+nq+Mu1pbFezfU1amqoVXNDrWorSlVdfkDtrR1be4SCAUmSzWbvLIEuT7qcLjdF8HMMw1AoGFDQ7+v4EfArGPBLisowJLvdIZcnTdm5BSqee4qGFY/uGL0rLJY7LbYnrJht5MiRuvPOOzVx4kQZhqGHHnpIF198sdavX68TTjih87q77777uL+u1q5dq2HDhunhhx9WSUmJli9frm9961uy2Wz613/916H8UoC4xwgfgF4xDENtLU0dRbC+Rk31taqvKlN1+X61NjUq4GtXMNix/57VaussgnaH89APh6xWW1IWwmg0qlDAr2DAp6Dfr0DAp3AgIMmQIcnhdMnp9sjtTlN2fqHyCovlzc1XZlaOMrJy5c3OU2ZOXlJ+b/oqNzdXd911l77+9a9LkjZs2KCLLrpIa9as0fDhw48a4Tue733ve9q+fbveeeedIUoMJAZG+AD0isViUYY3WxnebI0YM7HzccMw5GtrOVQCa9RcX6u66nJVl+1Xa1OD2v1NCoeCCodCMoxIx3lgssiQIZvdIbvDIbu9oxAeLoa2Qz83owAZhqFoNKJIOKxoJKxIOKxI5NCPcPizxw/93GKRDENyujxyud3ypGdoxJgJyi0s7ihy2R2lLjMrV2mZWbLZbEP+NSWCSCSip556Sm1tbVq0aJEkqb29XV/5ylf0xz/+UUVFvTvirampSbm5ubGMCiQkCh+AAbFYLErL8Cotw6vho8Z3Pm4YhgK+dvnaWxXwtcnf3ia/r00BX3vHx+2tam1qVEtzvdqamzqu8bUp0hzqKIjh0KEdTSw6fCPicAHs+K+l47+Wz/28F89HIp+VOcMwZFHHdYdzW6022ex22Wz2zv9a7Xa5M9Pl9qTLnZYhT3qGMrJylJmd1zlSl5mdK096pqzWwdtfL9lt3rxZixYtkt/vV0ZGhp577jlNmzZNknTDDTfo5JNP1sUXX9yr91q+fLmeeOIJvfzyy7GMDCQkCh+AmLBYLHKnpfd6HlooGOwsff72tkMff1YOQ8GAotGojGhU0WhE0UP/7fj50R8bh57/7LnPXidJbk/aodKWKZcnTU6XWw6XW65D/3Ue8V+n87OPk2nBRLyYPHmyNmzYoKamJj399NO69tpr9f7772vXrl165513tH79+l69z5YtW3TxxRfr1ltv1bnnnhvj1EDiYQ4fACBunH322Ro/frw8Ho9+//vfHzVaGolEZLVatWTJEr333nudj2/btk1nnHGGvvGNb+i///u/TUgNxD/+uQoAiBvRaFSBQEC33367vvGNbxz13IwZM/Tb3/5Wy5Yt63xs69atOvPMM3XttddS9oBuUPgAAKb48Y9/rKVLl2rUqFFqaWnRo48+qvfee0+vv/66ioqKjrtQY9SoURo7dqykjtu4Z555ps477zzdeOONqqyslCTZbDYVFBQM6dcCxDsKHwDAFNXV1fra176miooKZWVlaebMmXr99dd1zjnn9Or1Tz/9tGpqavTwww/r4Ycf7nx89OjRnZs5A+jAHD4AAIAkx94BAAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJjsIHAACQ5Ch8AAAASY7CBwAAkOQofAAAAEmOwgcAAJDkKHwAAABJ7v8PDvsH6ZqgH/cAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = titanic['Survived'].value_counts().index\n", + "shape = titanic['Survived'].value_counts().values\n", + "\n", + "plt.figure(figsize=(12,8))\n", + "plt.pie(shape, labels=shape, autopct = '%1.1f%%', shadow=True)\n", + "plt.title('Survived', fontsize=20)\n", + "plt.style.use('default')\n", + "plt.legend(labels)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df7310f1", + "metadata": {}, + "source": [ + "# How age affects survival rate(SR)?" + ] + }, + { + "cell_type": "markdown", + "id": "809114b6", + "metadata": {}, + "source": [ + "![frequency_distribution_age](images/frequency_age.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f8401ac6", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# kdeplot of survived by age\n", + "\n", + "plt.figure(figsize=(12,8))\n", + "g = sns.kdeplot(titanic_new[\"Age\"][(titanic_new[\"Survived\"] == 0)], color=\"Red\", shade=True)\n", + "g = sns.kdeplot(titanic_new[\"Age\"][(titanic_new[\"Survived\"] == 1)], ax=g, color=\"Blue\", shade=True)\n", + "g.set_xlabel(\"Age\")\n", + "g.set_ylabel(\"Frequency\")\n", + "g.set_title('Frequency of survived and not survived by age')\n", + "g = g.legend([\"Not Survived\",\"Survived\"])" + ] + }, + { + "cell_type": "markdown", + "id": "52061d20", + "metadata": {}, + "source": [ + "# How sex affects SR?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "490f536d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chance to survive if you are woman: 74.20 %\n", + "Chance to survive if you are man: 18.89 %\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Sex female male\n", + "Survived \n", + "0 81 468\n", + "1 233 109" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# barplot of survived by sex\n", + "\n", + "plt.figure(figsize=(12,8))\n", + "ax = sns.barplot(x=\"Sex\", y=\"Survived\", data=titanic)\n", + "ax.set_ylabel('% Survived')\n", + "ax.set_xlabel('Gender')\n", + "ax.set_title(\"Survival rate by gender\")\n", + "plt.show()\n", + "\n", + "\n", + "# survived rate by sex\n", + "dead_women = 81\n", + "dead_men = 468\n", + "\n", + "survived_women = 233\n", + "survived_men = 109\n", + "\n", + "total_women = dead_women + survived_women \n", + "total_men = dead_men + survived_men\n", + "percentage_to_survive_if_women = ( survived_women / total_women ) * 100\n", + "percentage_to_survive_if_men = ( survived_men / total_men ) * 100\n", + "\n", + "print(f'Chance to survive if you are woman: {percentage_to_survive_if_women:.2f} %')\n", + "print(f'Chance to survive if you are man: {percentage_to_survive_if_men:.2f} %')\n", + "\n", + "# pivot table\n", + "print()\n", + "\n", + "pd.pivot_table(titanic, index='Survived', columns='Sex', values='Ticket', aggfunc='count')" + ] + }, + { + "cell_type": "markdown", + "id": "822ccac4", + "metadata": {}, + "source": [ + "![frequency_distribution_sex](images/frequency_sex.png)" + ] + }, + { + "cell_type": "markdown", + "id": "4a298b65", + "metadata": {}, + "source": [ + "# How passenger's class affects SR?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1d60e055", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chance to survive if you stayed in first class: 62.96 %\n", + "Chance to survive if you stayed in second class: 47.28 %\n", + "Chance to survive if you stayed in third class: 24.24 %\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Pclass 1 2 3\n", + "Survived \n", + "0 80 97 372\n", + "1 136 87 119" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# barplot of survived by passenger's class\n", + "plt.figure(figsize=(12,8))\n", + "ax = sns.barplot(x=\"Pclass\", y=\"Survived\", data=titanic)\n", + "ax.set_ylabel('% Survived')\n", + "ax.set_xlabel('Passenger\\'s class')\n", + "ax.set_title('Survival rate by passenger\\'s class')\n", + "plt.show()\n", + "\n", + "# survived rate by passenger's class\n", + "dead_first_class = 80\n", + "dead_second_class = 97\n", + "dead_third_class = 372\n", + "\n", + "survived_first_class = 136 \n", + "survived_second_class = 87\n", + "survived_third_class = 119\n", + "\n", + "total_first_class = dead_first_class + survived_first_class\n", + "total_second_class = dead_second_class + survived_second_class\n", + "total_third_class = dead_third_class + survived_third_class\n", + "\n", + "percentage_to_survive_if_first_class = ( survived_first_class / total_first_class ) * 100\n", + "percentage_to_survive_if_second_class = ( survived_second_class / total_second_class ) * 100\n", + "percentage_to_surrvive_if_third_class = ( survived_third_class / total_third_class ) * 100\n", + "\n", + "\n", + "print(f'Chance to survive if you stayed in first class: {percentage_to_survive_if_first_class:.2f} %')\n", + "print(f'Chance to survive if you stayed in second class: {percentage_to_survive_if_second_class:.2f} %')\n", + "print(f'Chance to survive if you stayed in third class: {percentage_to_surrvive_if_third_class:.2f} %')\n", + "\n", + "\n", + "# pivot table\n", + "\n", + "pd.pivot_table(titanic, index='Survived', columns='Pclass', values='Ticket', aggfunc='count')" + ] + }, + { + "cell_type": "markdown", + "id": "35a6c367", + "metadata": {}, + "source": [ + "# How do siblings and spouses affect SR?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "63ad8a80", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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SibSp0123458
Survived
0398.097.015.012.015.05.07.0
1210.0112.013.04.03.0NaNNaN
\n", + "
" + ], + "text/plain": [ + "SibSp 0 1 2 3 4 5 8\n", + "Survived \n", + "0 398.0 97.0 15.0 12.0 15.0 5.0 7.0\n", + "1 210.0 112.0 13.0 4.0 3.0 NaN NaN" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# barplot of survived by siblings and spouses\n", + "\n", + "\n", + "plt.figure(figsize=(12,8))\n", + "ax = sns.barplot(x=\"SibSp\", y=\"Survived\", data=titanic)\n", + "ax.set_ylabel('% Survived')\n", + "ax.set_xlabel('Number of siblings or spouses')\n", + "ax.set_title('Survival rate by siblings or spouses')\n", + "plt.show()\n", + "\n", + "# survived rate by passenger's siblings or spouses\n", + "dead_without_siblings_or_spouses = 398\n", + "dead_having_1_siblings_or_spouses = 97\n", + "dead_having_2_siblings_or_spouses = 15\n", + "dead_having_3_siblings_or_spouses = 12\n", + "dead_having_4_siblings_or_spouses = 15\n", + "dead_having_5_siblings_or_spouses = 5\n", + "dead_having_8_siblings_or_spouses = 7\n", + "\n", + "survived_without_siblings_or_spouses = 210\n", + "survived_having_1_siblings_or_spouses = 112\n", + "survived_having_2_siblings_or_spouses = 13\n", + "survived_having_3_siblings_or_spouses = 4\n", + "survived_having_4_siblings_or_spouses = 3\n", + "survived_having_5_siblings_or_spouses = 0\n", + "survived_having_8_siblings_or_spouses = 0\n", + "\n", + "total_without_siblings_or_spouses = dead_without_siblings_or_spouses + survived_without_siblings_or_spouses\n", + "total_having_1_siblings_or_spouses = dead_having_1_siblings_or_spouses + survived_having_1_siblings_or_spouses\n", + "total_having_2_siblings_or_spouses = dead_having_2_siblings_or_spouses + survived_having_2_siblings_or_spouses\n", + "total_having_3_siblings_or_spouses = dead_having_3_siblings_or_spouses + survived_having_3_siblings_or_spouses\n", + "total_having_4_siblings_or_spouses = dead_having_4_siblings_or_spouses + survived_having_4_siblings_or_spouses\n", + "total_having_5_siblings_or_spouses = dead_having_5_siblings_or_spouses + survived_having_5_siblings_or_spouses\n", + "total_having_8_siblings_or_spouses = dead_having_8_siblings_or_spouses + survived_having_8_siblings_or_spouses\n", + "\n", + "\n", + "percentage_to_survive_if_without_siblings_or_spouses = ( survived_without_siblings_or_spouses / total_without_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_1_siblings_or_spouses = ( survived_having_1_siblings_or_spouses / total_having_1_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_2_siblings_or_spouses = ( survived_having_2_siblings_or_spouses / total_having_2_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_3_siblings_or_spouses = ( survived_having_3_siblings_or_spouses / total_having_3_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_4_siblings_or_spouses = ( survived_having_4_siblings_or_spouses / total_having_4_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_5_siblings_or_spouses = ( survived_having_5_siblings_or_spouses / total_having_5_siblings_or_spouses ) * 100\n", + "percentage_to_survive_if_having_8_siblings_or_spouses = ( survived_having_8_siblings_or_spouses / total_having_8_siblings_or_spouses ) * 100\n", + "\n", + "print(f'Chance to survive if you travel without any siblings or spouses: {percentage_to_survive_if_without_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 1 siblings or spouses: {percentage_to_survive_if_having_1_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 2 siblings or spouses: {percentage_to_survive_if_having_2_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 3 siblings or spouses: {percentage_to_survive_if_having_3_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 4 siblings or spouses: {percentage_to_survive_if_having_4_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 5 siblings or spouses: {percentage_to_survive_if_having_5_siblings_or_spouses:.2f} %')\n", + "print(f'Chance to survive if you travel with 8 siblings or spouses: {percentage_to_survive_if_having_8_siblings_or_spouses:.2f} %')\n", + "\n", + " \n", + "\n", + "# pivot table\n", + "\n", + "pd.pivot_table(titanic, index='Survived', columns='SibSp', values='Ticket', aggfunc='count')" + ] + }, + { + "cell_type": "markdown", + "id": "aca9732a", + "metadata": {}, + "source": [ + "# How port of embarkation affects SR?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8a9af4a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chance to survive if embarked from Cherbourg: 55.36 %\n", + "Chance to survive if embarked from Queenstown: 38.96 %\n", + "Chance to survive if embarked from Southampton: 33.90 %\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Embarked C Q S\n", + "Survived \n", + "0 75 47 427\n", + "1 93 30 217" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# barplot of survived by embarkation\n", + "plt.figure(figsize=(12,8))\n", + "ax = sns.barplot(x=\"Embarked\", y=\"Survived\", data=titanic)\n", + "ax.set_ylabel('% Survived')\n", + "ax.set_title('Survival rate by port of embarkation', size=12)\n", + "plt.show()\n", + "\n", + "# survived rate by embarkation\n", + "dead_embarked_C = 75\n", + "dead_embarked_Q = 47\n", + "dead_embarked_S = 427\n", + "\n", + "survived_embarked_C = 93\n", + "survived_embarked_Q = 30\n", + "survived_embarked_S = 219\n", + "\n", + "total_embarked_C = dead_embarked_C + survived_embarked_C\n", + "total_embarked_Q = dead_embarked_Q + survived_embarked_Q\n", + "total_embarked_S = dead_embarked_S + survived_embarked_S\n", + "\n", + "percentage_to_survive_if_embarked_C = ( survived_embarked_C / total_embarked_C ) * 100\n", + "percentage_to_survive_if_embarked_Q = ( survived_embarked_Q / total_embarked_Q ) * 100\n", + "percentage_to_survive_if_embarked_S = ( survived_embarked_S / total_embarked_S ) * 100\n", + "\n", + "\n", + "print(f'Chance to survive if embarked from Cherbourg: {percentage_to_survive_if_embarked_C:.2f} %')\n", + "print(f'Chance to survive if embarked from Queenstown: {percentage_to_survive_if_embarked_Q:.2f} %')\n", + "print(f'Chance to survive if embarked from Southampton: {percentage_to_survive_if_embarked_S:.2f} %')\n", + "\n", + "\n", + "## pivot table\n", + "\n", + "pd.pivot_table(titanic, index='Survived', columns='Embarked', values='Ticket', aggfunc='count')" + ] + } + ], + "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": "3.9.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Data/test.csv b/Data/test.csv new file mode 100644 index 0000000..2ed7ef4 --- /dev/null +++ b/Data/test.csv @@ -0,0 +1,419 @@ +PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q +893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S +894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q +895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S +896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S +897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S +898,3,"Connolly, Miss. 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. 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Description](#project-description) +- [Hypotheses / Questions](#hypotheses-questions) +- [Dataset](#dataset) +- [Cleaning](#cleaning) +- [Analysis](#analysis) +- [Model Training and Evaluation](#model-training-and-evaluation) +- [Conclusion](#conclusion) +- [Future Work](#future-work) +- [Workflow](#workflow) +- [Organization](#organization) +- [Links](#links) + +## Project Description +This project is about binary classification problem. The basic idea is to predict whether passenger survived or died in Titanic shipwreck. + +## Hypotheses / Questions +* How many people died or survived? +* How do age affect survival rate? +* How do gender affect survival rate +* How do passenger's class affect survival rate? +* How do siblings and spouses affect survival rate? +* How do port of embarkation affect survival rate? + +## Dataset +* The dataset was provided by Kaggle. +* Data contain information about passenger's name, age, gender, class, cabin, whether he or she has siblings or spouses on board and ho wmany, the same for parents and children and where embarked on board. +* In train dataset there is information whether passenger survived or not. + + + +## Cleaning +* AGE - filling with KNN-Imputation method, which takes information from all other variables and predict the most possible value for age. +* CABIN - dropped, because it has 687 out of 891 missing values and it's quite hard to be filled. +* EMBARKED - filling with mode, which is S. +* Outliers were detected based on boxplot analysis and dropped based on standard deviation approach. +* PassengerID, Name, Ticket were dropped while building the model. + +## Analysis +* 10 different models were used to solve this binary classification problem. (Logistic Regression, Random Forest Classifier, Catboost Classifier and so on.) +* The best performing models were KNN and Catboost. +* Combining both models lead to slightly worst results. + +## Model Training and Evaluation +* The final model was Catboost with accuracy score of 0.81 and kappa score 0.82 + +## Conclusion +* The resulst are satisfactorily but there is still place for improvements. + +## Future Work +* Different strategies for fillin missing values and handling outliers. +* NLP for extracting valuable information from names, like title. + +## Workflow +* The work was divided in 3 main stelps and 3 main notebooks. +* 1. Create a plan +* 2. Clean the data +* 3. Data visualization +* 4. Building a model + +## Organization +* A trello board was used to organize the work. +* The repo has two directories, one for the code and one for the data. +* The code directory includes three files, one for the data cleaning, visualizations, and predictions. +* The data directory contains initial data sets (train and test) and cleaned data set for visualizations and modeling. + + + +## Links + +[Dataset] (https://www.kaggle.com/c/titanic/data) +[Repository](https://github.com/) +[Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) +[Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack) diff --git a/your-project/README.md b/your-project/README.md deleted file mode 100644 index 9563cd7..0000000 --- a/your-project/README.md +++ /dev/null @@ -1,72 +0,0 @@ -Ironhack Logo - -# Title of My Project -*[Your Name]* - -*[Your Cohort, Campus & Date]* - -## Content -- [Project Description](#project-description) -- [Hypotheses / Questions](#hypotheses-questions) -- [Dataset](#dataset) -- [Cleaning](#cleaning) -- [Analysis](#analysis) -- [Model Training and Evaluation](#model-training-and-evaluation) -- [Conclusion](#conclusion) -- [Future Work](#future-work) -- [Workflow](#workflow) -- [Organization](#organization) -- [Links](#links) - -## Project Description -Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show. - -## Hypotheses / Questions -* What data/business/research/personal question you would like to answer? -* What is the context for the question and the possible scientific or business application? -* What are the hypotheses you would like to test in order to answer your question? -Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required. - -## Dataset -* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset. -* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo. -* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary. -* If the question cannot be answered with the available data, why not? What data would you need to answer it better? - -## Cleaning -Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process. - -## Analysis -* Overview the general steps you went through to analyze your data in order to test your hypothesis. -* Document each step of your data exploration and analysis. -* Include charts to demonstrate the effect of your work. -* If you used Machine Learning in your final project, describe your feature selection process. - -## Model Training and Evaluation -*Include this section only if you chose to include ML in your project.* -* Describe how you trained your model, the results you obtained, and how you evaluated those results. - -## Conclusion -* Summarize your results. What do they mean? -* What can you say about your hypotheses? -* Interpret your findings in terms of the questions you try to answer. - -## Future Work -Address any questions you were unable to answer, or any next steps or future extensions to your project. - -## Workflow -Outline the workflow you used in your project. What were the steps? -How did you test the accuracy of your analysis and/or machine learning algorithm? - -## Organization -How did you organize your work? Did you use any tools like a trello or kanban board? - -What does your repository look like? Explain your folder and file structure. - -## Links -Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project. - - -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) From e4619c4e8768326cc5e2f88496fcaec343c51232 Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 29 Jul 2021 22:13:12 +0200 Subject: [PATCH 2/6] 29/07/2021 --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2be27ea..b231106 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ This project is about binary classification problem. The basic idea is to predic * NLP for extracting valuable information from names, like title. ## Workflow -* The work was divided in 3 main stelps and 3 main notebooks. +* The work was divided in 4 main steps. * 1. Create a plan * 2. Clean the data * 3. Data visualization @@ -75,7 +75,7 @@ This project is about binary classification problem. The basic idea is to predic ## Links -[Dataset] (https://www.kaggle.com/c/titanic/data) +[Dataset](https://www.kaggle.com/c/titanic/data) [Repository](https://github.com/) [Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) [Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack) From 6c7f7687fb1b165740a83ca3e9e80a508b087e89 Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 29 Jul 2021 22:15:07 +0200 Subject: [PATCH 3/6] 29/07/2021 --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index b231106..40d83c3 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,6 @@ This project is about binary classification problem. The basic idea is to predic ## Links -[Dataset](https://www.kaggle.com/c/titanic/data) -[Repository](https://github.com/) +[Dataset](https://www.kaggle.com/c/titanic/data) [Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) [Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack) From 6fd0c08ade8224dfdbfb484025e6c0536e396804 Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 29 Jul 2021 22:15:50 +0200 Subject: [PATCH 4/6] 29/07/2021 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 40d83c3..a3b49fb 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,6 @@ This project is about binary classification problem. The basic idea is to predic ## Links -[Dataset](https://www.kaggle.com/c/titanic/data) +[Dataset](https://www.kaggle.com/c/titanic/data) [Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) [Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack) From 4364600439f68489a4078f1303ef9c7efd4e3469 Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 29 Jul 2021 22:16:22 +0200 Subject: [PATCH 5/6] 29/07/2021 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a3b49fb..72136b2 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,6 @@ This project is about binary classification problem. The basic idea is to predic ## Links -[Dataset](https://www.kaggle.com/c/titanic/data) +[Dataset](https://www.kaggle.com/c/titanic/data) [Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) [Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack) From e22a062fb7040474cd054b86f3159cbc46d38269 Mon Sep 17 00:00:00 2001 From: Nikolov-A Date: Thu, 5 Aug 2021 16:50:08 +0200 Subject: [PATCH 6/6] Update presentation link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 72136b2..ede2b97 100644 --- a/README.md +++ b/README.md @@ -76,5 +76,5 @@ This project is about binary classification problem. The basic idea is to predic ## Links [Dataset](https://www.kaggle.com/c/titanic/data) -[Presentation](https://drive.google.com/file/d/1RwQuYr2FTdweED14OyLF0xVsUco3t5os/view?usp=sharing) +[Presentation](https://drive.google.com/file/d/1dGl9jeVz66l16QCj2b_hyuX--CdTVrVK/view?usp=sharing) [Trello](https://trello.com/b/JcT6ox3A/titanic-final-project-ironhack)