"
],
"text/plain": [
" Principal terms age Gender weekend Bechalor High School or Below \\\n",
"0 1000 30 45 0 0 0 1 \n",
"1 1000 30 33 1 0 1 0 \n",
"2 1000 15 27 0 0 0 0 \n",
"3 1000 30 28 1 1 0 0 \n",
"4 1000 30 29 0 1 0 0 \n",
"\n",
" college \n",
"0 0 \n",
"1 0 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = Feature\n",
"X[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"What are our lables?"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = df['loan_status'].values\n",
"y[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Normalize Data "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.51578458, 0.92071769, 2.33152555, -0.42056004, -1.20577805,\n",
" -0.38170062, 1.13639374, -0.86968108],\n",
" [ 0.51578458, 0.92071769, 0.34170148, 2.37778177, -1.20577805,\n",
" 2.61985426, -0.87997669, -0.86968108],\n",
" [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
" -0.38170062, -0.87997669, 1.14984679],\n",
" [ 0.51578458, 0.92071769, -0.48739188, 2.37778177, 0.82934003,\n",
" -0.38170062, -0.87997669, 1.14984679],\n",
" [ 0.51578458, 0.92071769, -0.3215732 , -0.42056004, 0.82934003,\n",
" -0.38170062, -0.87997669, 1.14984679]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X= preprocessing.StandardScaler().fit(X).transform(X)\n",
"X[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Classification "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
"You should use the following algorithm:\n",
"- K Nearest Neighbor(KNN)\n",
"- Decision Tree\n",
"- Support Vector Machine\n",
"- Logistic Regression\n",
"\n",
"\n",
"\n",
"__ Notice:__ \n",
"- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
"- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
"- You should include the code of the algorithm in the following cells."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K Nearest Neighbor(KNN)\n",
"Notice: You should find the best k to build the model with the best accuracy. \n",
"**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluation using Test set"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import jaccard_similarity_score\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.metrics import log_loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, download and load the test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Test set for evaluation "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"test_df = pd.read_csv('loan_test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Report\n",
"You should be able to report the accuracy of the built model using different evaluation metrics:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Algorithm | Jaccard | F1-score | LogLoss |\n",
"|--------------------|---------|----------|---------|\n",
"| KNN | ? | ? | NA |\n",
"| Decision Tree | ? | ? | NA |\n",
"| SVM | ? | ? | NA |\n",
"| LogisticRegression | ? | ? | ? |"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"
Want to learn more?
\n",
"\n",
"IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler\n",
"\n",
"Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio\n",
"\n",
"
Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.
![](\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\")