diff --git a/.ipynb_checkpoints/Decision Trees - Titanic-checkpoint.ipynb b/.ipynb_checkpoints/Decision Trees - Titanic-checkpoint.ipynb new file mode 100644 index 0000000..a3d88b6 --- /dev/null +++ b/.ipynb_checkpoints/Decision Trees - Titanic-checkpoint.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# You are given data from the Titanic, in data.csv.\n", + "\n", + "# For this challenge we need to guess whether the individuals from the dataset had survived or not. Use the provided features and either modify, delete or add new features based on existing ones. This is a very core part of being a data scientist.\n", + "\n", + "# After you have massaged the data into the form that makes you happy, then use a DecisionTreeClassifier from sklearn and try and get the highest accuracy you can get. Try adjusting the depth of the tree to vary accuracy.\n", + "\n", + "# Finally try to perform cross validation.\n", + "\n", + "# Note the data-dictionary.txt provides information on the fields in the CSV file.\n", + "\n", + "# - http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn import linear_model\n", + "from pandas.plotting import scatter_matrix\n", + "from itertools import combinations\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = pd.read_csv('data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
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4503Allen, Mr. William Henry035.0003734508.0500NaNS
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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", + " Name Sex Age SibSp Parch \\\n", + "0 Braund, Mr. Owen Harris 0 22.0 1 0 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 1 38.0 1 0 \n", + "2 Heikkinen, Miss. Laina 1 26.0 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 35.0 1 0 \n", + "4 Allen, Mr. William Henry 0 35.0 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 A/5 21171 7.2500 NaN S \n", + "1 PC 17599 71.2833 C85 C \n", + "2 STON/O2. 3101282 7.9250 NaN S \n", + "3 113803 53.1000 C123 S \n", + "4 373450 8.0500 NaN S " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = df[['Pclass', 'Fare', 'SibSp', 'Sex']]\n", + "y = df.Survived" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n", + "model = tree.DecisionTreeClassifier().fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9261744966442953" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.78983050847457625" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_hat = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([152, 23, 39, 81], dtype=int64)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(y_test,y_hat).ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy : 0.789830508475\n", + "Recall : 0.675\n", + "Precision : 0.778846153846\n" + ] + } + ], + "source": [ + "TN,FP,FN,TP = confusion_matrix(y_test,y_hat).ravel()\n", + "TOTAL = TP + TN + FP + FN\n", + "accuracy = (TP + TN) / TOTAL\n", + "recall = TP / (TP + FN)\n", + "precision = TP / (TP + FP)\n", + "print (\"Accuracy:\",accuracy)\n", + "print (\"Recall:\", recall)\n", + "print (\"Precision:\", precision)" + ] + } + ], + "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": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}