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‎Applied Machine Learning in Python/Assignment 1.ipynb‎

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‎Applied Machine Learning in Python/Assignment 2.ipynb‎

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‎Applied Machine Learning in Python/Assignment 3.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"\n",
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"_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Assignment 3 - Evaluation\n",
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"\n",
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"In this assignment you will train several models and evaluate how effectively they predict instances of fraud using data based on [this dataset from Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud).\n",
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" \n",
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"Each row in `fraud_data.csv` corresponds to a credit card transaction. Features include confidential variables `V1` through `V28` as well as `Amount` which is the amount of the transaction. \n",
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" \n",
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"The target is stored in the `class` column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud."
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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": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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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": "markdown",
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"metadata": {},
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"source": [
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"### Question 1\n",
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"Import the data from `fraud_data.csv`. What percentage of the observations in the dataset are instances of fraud?\n",
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"\n",
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"*This function should return a float between 0 and 1.* "
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_one():\n",
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" \n",
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" # Your code here\n",
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" \n",
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" return df.iloc[:,-1].mean()# Return your answer\n"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Use X_train, X_test, y_train, y_test for all of the following questions\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"df = pd.read_csv('fraud_data.csv')\n",
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"\n",
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"X = df.iloc[:,:-1]\n",
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"y = df.iloc[:,-1]\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Question 2\n",
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"\n",
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"Using `X_train`, `X_test`, `y_train`, and `y_test` (as defined above), train a dummy classifier that classifies everything as the majority class of the training data. What is the accuracy of this classifier? What is the recall?\n",
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"\n",
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"*This function should a return a tuple with two floats, i.e. `(accuracy score, recall score)`.*"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_two():\n",
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" from sklearn.dummy import DummyClassifier\n",
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" from sklearn.metrics import recall_score\n",
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" clf = DummyClassifier(\"most_frequent\",random_state = 0)\n",
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" clf.fit(X_train,y_train)\n",
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" acc = clf.score(X_test,y_test)\n",
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" recall = recall_score(y_test,clf.predict(X_test),'binary')\n",
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" # Your code here\n",
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" \n",
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" return (acc,recall)# Return your answer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Question 3\n",
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"\n",
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"Using X_train, X_test, y_train, y_test (as defined above), train a SVC classifer using the default parameters. What is the accuracy, recall, and precision of this classifier?\n",
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"\n",
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"*This function should a return a tuple with three floats, i.e. `(accuracy score, recall score, precision score)`.*"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_three():\n",
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" from sklearn.metrics import recall_score, precision_score\n",
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" from sklearn.svm import SVC\n",
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" \n",
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" # Your code here\n",
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" clf = SVC()\n",
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" clf.fit(X_train,y_train)\n",
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" acc = clf.score(X_test,y_test)\n",
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" recall = recall_score(y_test,clf.predict(X_test),'binary')\n",
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" precision = precision_score(y_test,clf.predict(X_test),'binary')\n",
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" \n",
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" return (acc,recall,precision)# Return your answer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Question 4\n",
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"\n",
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"Using the SVC classifier with parameters `{'C': 1e9, 'gamma': 1e-07}`, what is the confusion matrix when using a threshold of -220 on the decision function. Use X_test and y_test.\n",
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"\n",
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"*This function should return a confusion matrix, a 2x2 numpy array with 4 integers.*"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_four():\n",
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" from sklearn.metrics import confusion_matrix\n",
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" from sklearn.svm import SVC\n",
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"\n",
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" # Your code here\n",
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" clf = SVC(C=1e9,gamma=1e-07)\n",
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" clf.fit(X_train,y_train)\n",
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" temp = clf.decision_function(X_test)\n",
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" ans = confusion_matrix(y_test,np.greater(temp,-220),)\n",
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" \n",
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" return ans# Return your answer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Question 5\n",
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"\n",
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"Train a logisitic regression classifier with default parameters using X_train and y_train.\n",
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"\n",
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"For the logisitic regression classifier, create a precision recall curve and a roc curve using y_test and the probability estimates for X_test (probability it is fraud).\n",
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"\n",
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"Looking at the precision recall curve, what is the recall when the precision is `0.75`?\n",
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"\n",
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"Looking at the roc curve, what is the true positive rate when the false positive rate is `0.16`?\n",
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"\n",
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"*This function should return a tuple with two floats, i.e. `(recall, true positive rate)`.*"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_five():\n",
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" \n",
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" # Your code here\n",
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" from sklearn.linear_model import LogisticRegression\n",
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" from sklearn.metrics import precision_recall_curve\n",
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" from sklearn.metrics import roc_curve\n",
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" #import matplotlib.pyplot as plt\n",
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" #%matplotlib inline\n",
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" \n",
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" clf = LogisticRegression(n_jobs=-1)\n",
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" clf.fit(X_train,y_train)\n",
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" p,r,_ = precision_recall_curve(y_test,clf.predict_proba(X_test)[:,1],)\n",
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" fpr,tpr,_ = roc_curve(y_test,clf.predict_proba(X_test)[:,1],)\n",
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" \n",
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" #plt.plot(fpr,tpr)\n",
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" #plt.xlabel('fpr')\n",
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" #plt.ylabel('tpr')\n",
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" #plt.show()\n",
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" return (0.8,0.9)# Return your answer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Question 6\n",
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"\n",
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"Perform a grid search over the parameters listed below for a Logisitic Regression classifier, using recall for scoring and the default 3-fold cross validation.\n",
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"\n",
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"`'penalty': ['l1', 'l2']`\n",
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"\n",
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"`'C':[0.01, 0.1, 1, 10, 100]`\n",
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"\n",
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"From `.cv_results_`, create an array of the mean test scores of each parameter combination. i.e.\n",
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"\n",
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"| \t| `l1` \t| `l2` \t|\n",
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"|:----:\t|----\t|----\t|\n",
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"| **`0.01`** \t| ?\t| ? \t|\n",
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"| **`0.1`** \t| ?\t| ? \t|\n",
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"| **`1`** \t| ?\t| ? \t|\n",
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"| **`10`** \t| ?\t| ? \t|\n",
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"| **`100`** \t| ?\t| ? \t|\n",
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"\n",
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"<br>\n",
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"\n",
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"*This function should return a 5 by 2 numpy array with 10 floats.* \n",
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"\n",
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"*Note: do not return a DataFrame, just the values denoted by '?' above in a numpy array.*"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def answer_six(): \n",
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" from sklearn.model_selection import GridSearchCV\n",
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" from sklearn.linear_model import LogisticRegression\n",
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"\n",
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" # Your code here\n",
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" params = {'penalty': ['l1', 'l2'],\n",
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" 'C':[0.01, 0.1, 1, 10, 100]}\n",
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" clf = GridSearchCV(LogisticRegression(n_jobs=-1),params,scoring='recall',n_jobs=-1)\n",
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" clf.fit(X_train,y_train)\n",
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" ans = clf.cv_results_['mean_test_score'].reshape(5,2)\n",
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" return ans# Return your answer"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Use the following function to help visualize results from the grid search\n",
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"def GridSearch_Heatmap(scores):\n",
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" %matplotlib notebook\n",
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" import seaborn as sns\n",
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" import matplotlib.pyplot as plt\n",
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" plt.figure()\n",
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" sns.heatmap(scores.reshape(5,2), xticklabels=['l1','l2'], yticklabels=[0.01, 0.1, 1, 10, 100])\n",
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" plt.yticks(rotation=0);\n",
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"\n",
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"#GridSearch_Heatmap(answer_six())"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"coursera": {
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"course_slug": "python-machine-learning",
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"graded_item_id": "5yX9Z",
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"launcher_item_id": "eqnV3",
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"part_id": "Msnj0"
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},
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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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"codemirror_mode": {
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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