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Machine-Learning-Collection/ML/Kaggles/Titanic/FirstKaggle_Titanic.ipynb
Aladdin Persson 65b8c80495 Initial commit
2021-01-30 21:49:15 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "electoral-scientist",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "surrounded-albert",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"train.csv\")\n",
"test = pd.read_csv(\"test.csv\")\n",
"test_ids = test[\"PassengerId\"]\n",
"\n",
"def clean(data):\n",
" data = data.drop([\"Ticket\", \"PassengerId\", \"Name\", \"Cabin\"], axis=1)\n",
" \n",
" cols = [\"SibSp\", \"Parch\", \"Fare\", \"Age\"]\n",
" for col in cols:\n",
" data[col].fillna(data[col].median(), inplace=True)\n",
" \n",
" data.Embarked.fillna(\"U\", inplace=True)\n",
" return data\n",
"\n",
"data = clean(data)\n",
"test = clean(test)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "electronic-wyoming",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
"0 0 3 male 22.0 1 0 7.2500 S\n",
"1 1 1 female 38.0 1 0 71.2833 C\n",
"2 1 3 female 26.0 0 0 7.9250 S"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "legendary-conditions",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['female' 'male']\n",
"['C' 'Q' 'S' 'U']\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>2</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
"0 0 3 1 22.0 1 0 7.2500 2\n",
"1 1 1 0 38.0 1 0 71.2833 0\n",
"2 1 3 0 26.0 0 0 7.9250 2\n",
"3 1 1 0 35.0 1 0 53.1000 2\n",
"4 0 3 1 35.0 0 0 8.0500 2"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import preprocessing\n",
"le = preprocessing.LabelEncoder()\n",
"columns = [\"Sex\", \"Embarked\"]\n",
"\n",
"for col in columns:\n",
" data[col] = le.fit_transform(data[col])\n",
" test[col] = le.transform(test[col])\n",
" print(le.classes_)\n",
" \n",
"data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "assumed-screening",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"y = data[\"Survived\"]\n",
"X = data.drop(\"Survived\", axis=1)\n",
"\n",
"X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "industrial-internship",
"metadata": {},
"outputs": [],
"source": [
"clf = LogisticRegression(random_state=0, max_iter=1000).fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fifteen-enemy",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8888888888888888"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = clf.predict(X_val)\n",
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_val, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "juvenile-anthropology",
"metadata": {},
"outputs": [],
"source": [
"submission_preds = clf.predict(test)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "virgin-settlement",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({\"PassengerId\": test_ids.values,\n",
" \"Survived\": submission_preds,\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "tribal-bidding",
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"submission.csv\", index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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