Skip to content

Kiki Chandra's submission (forgot to submit pull request earlier) #10

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
skchandra wants to merge 7 commits into
base: master
Choose a base branch
from
Open

Kiki Chandra's submission (forgot to submit pull request earlier) #10

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
508690b
Updated explanation
skchandra Jan 26, 2016
07b2669
DataQuest iteration completed
skchandra Jan 28, 2016
6fec0a3
Improved accuracy to 0.7799
skchandra Jan 29, 2016
f5960d9
Commented code
skchandra Jan 31, 2016
bbc4b7b
New data cleaning done
skchandra Feb 1, 2016
d4b484c
finished, need to comment code/clean up
skchandra Feb 2, 2016
9266e17
Finished warmup project
skchandra Feb 2, 2016
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3,028 changes: 3,028 additions & 0 deletions .ipynb_checkpoints/DataExploration-checkpoint.ipynb
View file
Open in desktop

Large diffs are not rendered by default.

392 changes: 392 additions & 0 deletions .ipynb_checkpoints/model_iteration_1-checkpoint.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,392 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shivali Chandra <br>\n",
"First iteration of model for Titanic Kaggle dataset. <br>\n",
"1/27/16 <br>\n",
"Initial Score: 0.75120 <br>\n",
"Random forests Score: 0.7535 <br>\n",
"Adding more columns Score: 0.7799"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First steps: load libraries used, read from training file and show basic statistics of file"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass 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": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn import cross_validation\n",
"import numpy as np\n",
"\n",
"titanic = pd.read_csv(\"train.csv\")\n",
"titanic.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleaning code, filling in NaN values and replacing text values with number codes: "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())\n",
"\n",
"titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0\n",
"titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1\n",
"\n",
"titanic['Embarked'] = titanic['Embarked'].fillna('S')\n",
"titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0\n",
"titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1\n",
"titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Defining columns used to predict target, generating cross validation folds for the dataset (with random state set to ensure splits are the same every time), initializing predictors and target, training algorithm, and making predictions:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.78787878787878773"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = pd.read_csv('test.csv')\n",
"test['Age'] = test['Age'].fillna(titanic['Age'].median())\n",
"\n",
"test.loc[test['Sex'] == 'male', 'Sex'] = 0\n",
"test.loc[test['Sex'] == 'female', 'Sex'] = 1\n",
"\n",
"test['Embarked'] = test['Embarked'].fillna('S')\n",
"test.loc[test['Embarked'] == 'S', 'Embarked'] = 0\n",
"test.loc[test['Embarked'] == 'C', 'Embarked'] = 1\n",
"test.loc[test['Embarked'] == 'Q', 'Embarked'] = 2\n",
"\n",
"test['Fare'] = test['Fare'].fillna(titanic['Fare'].median())\n",
"\n",
"alg = LogisticRegression(random_state=1)\n",
"scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n",
"scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.819304152637\n"
]
}
],
"source": [
"from sklearn import cross_validation\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']\n",
"titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))\n",
"test['FamilySize'] = test['SibSp'] + test['Parch']\n",
"test['NameLength'] = test['Name'].apply(lambda x: len(x))\n",
"\n",
"predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n",
"\n",
"# Initialize our algorithm with the default paramters\n",
"# n_estimators is the number of trees we want to make\n",
"# min_samples_split is the minimum number of rows we need to make a split\n",
"# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n",
"alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=10, min_samples_leaf=5)\n",
"# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)\n",
"scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n",
"\n",
"# Take the mean of the scores (because we have one for each fold)\n",
"print(scores.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
"\n",
"alg = LinearRegression()\n",
"kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n",
"scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n",
"scores.mean()\n",
"\n",
"predictions = []\n",
"for train, test in kf:\n",
" train_predictors = (titanic[predictors].iloc[train,:])\n",
" train_target = titanic['Survived'].iloc[train]\n",
" alg.fit(train_predictors, train_target)\n",
" test_predictions = alg.predict(titanic[predictors].iloc[test,:])\n",
" predictions.append(test_predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Concatenating three prediction np arrays into one, and mapping the predictions to outcomes. Then, calculating the accuracy: "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"predictions = np.concatenate(predictions, axis=0)\n",
"\n",
"predictions[predictions > 0.5] = 1\n",
"predictions[predictions <= 0.5] = 0\n",
"\n",
"accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Computing accuracy score for all cross validation folds, and taking mean of scores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleaning test data. Filling missing NaN values and replacing text values with number codes: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating submission for competition - training algorithm, making predictions, and creating dataframe with the columns needed: "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alg.fit(titanic[predictors], titanic['Survived'])\n",
"\n",
"predictions = alg.predict(test[predictors])\n",
"\n",
"submission = pd.DataFrame({\n",
" 'PassengerId': test['PassengerId'],\n",
" 'Survived': predictions\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission.to_csv('kaggle.csv', index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Loading