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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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Initial commit
This commit is contained in:
Aladdin Persson
364
ML/Kaggles/Titanic/FirstKaggle_Titanic.ipynb
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364
ML/Kaggles/Titanic/FirstKaggle_Titanic.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "electoral-scientist",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "surrounded-albert",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv(\"train.csv\")\n",
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"test = pd.read_csv(\"test.csv\")\n",
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"test_ids = test[\"PassengerId\"]\n",
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"\n",
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"def clean(data):\n",
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" data = data.drop([\"Ticket\", \"PassengerId\", \"Name\", \"Cabin\"], axis=1)\n",
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" \n",
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" cols = [\"SibSp\", \"Parch\", \"Fare\", \"Age\"]\n",
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" for col in cols:\n",
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" data[col].fillna(data[col].median(), inplace=True)\n",
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" \n",
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" data.Embarked.fillna(\"U\", inplace=True)\n",
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" return data\n",
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"\n",
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"data = clean(data)\n",
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"test = clean(test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "electronic-wyoming",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <th></th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Fare</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" <th>0</th>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>female</td>\n",
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" <td>38.0</td>\n",
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" <td>1</td>\n",
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" <td>C</td>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>female</td>\n",
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" <td>0</td>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
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"0 0 3 male 22.0 1 0 7.2500 S\n",
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"1 1 1 female 38.0 1 0 71.2833 C\n",
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"2 1 3 female 26.0 0 0 7.9250 S"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.head(3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "legendary-conditions",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['female' 'male']\n",
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"['C' 'Q' 'S' 'U']\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<style scoped>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Fare</th>\n",
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" <th>Embarked</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <td>1</td>\n",
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" <tr>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
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"0 0 3 1 22.0 1 0 7.2500 2\n",
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"1 1 1 0 38.0 1 0 71.2833 0\n",
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"2 1 3 0 26.0 0 0 7.9250 2\n",
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"3 1 1 0 35.0 1 0 53.1000 2\n",
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"4 0 3 1 35.0 0 0 8.0500 2"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn import preprocessing\n",
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"le = preprocessing.LabelEncoder()\n",
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"columns = [\"Sex\", \"Embarked\"]\n",
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"\n",
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"for col in columns:\n",
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" data[col] = le.fit_transform(data[col])\n",
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" test[col] = le.transform(test[col])\n",
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" print(le.classes_)\n",
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" \n",
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"data.head(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "assumed-screening",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"y = data[\"Survived\"]\n",
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"X = data.drop(\"Survived\", axis=1)\n",
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"\n",
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"X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "industrial-internship",
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"metadata": {},
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"outputs": [],
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"source": [
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"clf = LogisticRegression(random_state=0, max_iter=1000).fit(X_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fifteen-enemy",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.8888888888888888"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"predictions = clf.predict(X_val)\n",
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"from sklearn.metrics import accuracy_score\n",
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"accuracy_score(y_val, predictions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "juvenile-anthropology",
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"metadata": {},
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"outputs": [],
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"source": [
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"submission_preds = clf.predict(test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "virgin-settlement",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame({\"PassengerId\": test_ids.values,\n",
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" \"Survived\": submission_preds,\n",
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" })"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "tribal-bidding",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.to_csv(\"submission.csv\", index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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